{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3171a0f1-eaeb-4da0-a1d2-bd17bd02064f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error: package or namespace load failed for ‘SeuratDisk’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]):\n there is no package called ‘Seurat’\n",
     "output_type": "error",
     "traceback": [
      "Error: package or namespace load failed for ‘SeuratDisk’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]):\n there is no package called ‘Seurat’\nTraceback:\n",
      "1. tryCatch({\n .     attr(package, \"LibPath\") <- which.lib.loc\n .     ns <- loadNamespace(package, lib.loc)\n .     env <- attachNamespace(ns, pos = pos, deps, exclude, include.only)\n . }, error = function(e) {\n .     P <- if (!is.null(cc <- conditionCall(e))) \n .         paste(\" in\", deparse(cc)[1L])\n .     else \"\"\n .     msg <- gettextf(\"package or namespace load failed for %s%s:\\n %s\", \n .         sQuote(package), P, conditionMessage(e))\n .     if (logical.return && !quietly) \n .         message(paste(\"Error:\", msg), domain = NA)\n .     else stop(msg, call. = FALSE, domain = NA)\n . })",
      "2. tryCatchList(expr, classes, parentenv, handlers)",
      "3. tryCatchOne(expr, names, parentenv, handlers[[1L]])",
      "4. value[[3L]](cond)",
      "5. stop(msg, call. = FALSE, domain = NA)",
      "6. .handleSimpleError(function (cnd) \n . {\n .     watcher$capture_plot_and_output()\n .     cnd <- sanitize_call(cnd)\n .     watcher$push(cnd)\n .     switch(on_error, continue = invokeRestart(\"eval_continue\"), \n .         stop = invokeRestart(\"eval_stop\"), error = NULL)\n . }, \"package or namespace load failed for ‘SeuratDisk’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]):\\n there is no package called ‘Seurat’\", \n .     base::quote(NULL))"
     ]
    }
   ],
   "source": [
    "library(SeuratDisk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6890880-8888-4844-aba6-a3b4bf9ef490",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in packageVersion(\"Seurat\"): there is no package called ‘Seurat’\n",
     "output_type": "error",
     "traceback": [
      "Error in packageVersion(\"Seurat\"): there is no package called ‘Seurat’\nTraceback:\n",
      "1. stop(packageNotFoundError(pkg, lib.loc, sys.call()))"
     ]
    }
   ],
   "source": [
    "packageVersion(\"Seurat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4cc5ff65-1f26-4445-881e-b76d68ff49c7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "library(Seurat)\n",
    "library(patchwork)\n",
    "library(readr)\n",
    "library(RColorBrewer)\n",
    "library(ggplot2)\n",
    "library(VisCello)\n",
    "library(viridis)\n",
    "library(forcats)\n",
    "library(cowplot)\n",
    "library(GSEABase)\n",
    "library(ComplexHeatmap)\n",
    "library(DoubletFinder)\n",
    "library(irlba)\n",
    "library(AUCell) \n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5149dc0a-4499-4af9-a7a8-f41725c4ece0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] ‘2.0.3’"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(DoubletFinder)\n",
    "packageVersion(\"DoubletFinder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d93c075d-3d35-475b-adbc-1e4522bc6110",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(AUCell) # x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8aa73cd-ae8d-47c5-99da-cfc35257585f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_dir_MACS = \"../data/geo/MACS/\"  # 替换为实际路径\n",
    "data_dir_H21 = \"../data/geo/H21/\"\n",
    "data_dir_H14 = \"../data/geo/H14/\"\n",
    "data_dir_H41 = \"../data/geo/H41/\"\n",
    "data_dir_H39 = \"../data/geo/H39/\"\n",
    "data_dir_H38 = \"../data/geo/H38/\"\n",
    "data_dir_H36 = \"../data/geo/H36/\"\n",
    "data_dir_H35 = \"../data/geo/H35/\"\n",
    "data_dir_H34 = \"../data/geo/H34/\"\n",
    "data_dir_H33 = \"../data/geo/H33/\"\n",
    "data_dir_H32 = \"../data/geo/H32/\"\n",
    "data_dir_H24 = \"../data/geo/H24/\"\n",
    "data_dir_H23 = \"../data/geo/H23/\"\n",
    "\n",
    "MACS <- Read10X(data.dir = data_dir_MACS)\n",
    "H21 <- Read10X(data.dir = data_dir_H21)\n",
    "H23 <- Read10X(data.dir = data_dir_H23)\n",
    "H24 <- Read10X(data.dir = data_dir_H24)\n",
    "H32 <- Read10X(data.dir = data_dir_H32)\n",
    "H33 <- Read10X(data.dir = data_dir_H33)\n",
    "H34 <- Read10X(data.dir = data_dir_H34)\n",
    "H35 <- Read10X(data.dir = data_dir_H35)\n",
    "H36 <- Read10X(data.dir = data_dir_H36)\n",
    "H38 <- Read10X(data.dir = data_dir_H38)\n",
    "H39 <- Read10X(data.dir = data_dir_H39)\n",
    "H41 <- Read10X(data.dir = data_dir_H41)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2369bc5-ff68-494e-b17a-0a03d6d70bb3",
   "metadata": {},
   "source": [
    "# Initialize the Seurat object with the raw (non-normalized data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "781b7b49-3532-4efd-9faf-8fe998890b73",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26337 features across 7672 samples within 1 assay \n",
       "Active assay: RNA (26337 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26854 features across 8930 samples within 1 assay \n",
       "Active assay: RNA (26854 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27155 features across 12401 samples within 1 assay \n",
       "Active assay: RNA (27155 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26628 features across 9433 samples within 1 assay \n",
       "Active assay: RNA (26628 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "25632 features across 4853 samples within 1 assay \n",
       "Active assay: RNA (25632 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "25511 features across 8437 samples within 1 assay \n",
       "Active assay: RNA (25511 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "25452 features across 7189 samples within 1 assay \n",
       "Active assay: RNA (25452 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26154 features across 8406 samples within 1 assay \n",
       "Active assay: RNA (26154 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27311 features across 11200 samples within 1 assay \n",
       "Active assay: RNA (27311 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "26791 features across 8507 samples within 1 assay \n",
       "Active assay: RNA (26791 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27092 features across 10242 samples within 1 assay \n",
       "Active assay: RNA (27092 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "An object of class Seurat \n",
       "27444 features across 9351 samples within 1 assay \n",
       "Active assay: RNA (27444 features, 0 variable features)\n",
       " 1 layer present: counts"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MACS <- CreateSeuratObject(counts = MACS, project = \"H14_MACS\", min.cells = 3, min.features = 100)\n",
    "MACS\n",
    "H21 <- CreateSeuratObject(counts = H21, project = \"H21\", min.cells = 3, min.features = 100)\n",
    "H21\n",
    "H23 <- CreateSeuratObject(counts = H23, project = \"H23\", min.cells = 3, min.features = 100)\n",
    "H23\n",
    "H24 <- CreateSeuratObject(counts = H24, project = \"H24\", min.cells = 3, min.features = 100)\n",
    "H24\n",
    "H32 <- CreateSeuratObject(counts = H32, project = \"H32\", min.cells = 3, min.features = 100)\n",
    "H32\n",
    "H33 <- CreateSeuratObject(counts = H33, project = \"H33\", min.cells = 3, min.features = 100)\n",
    "H33\n",
    "H34 <- CreateSeuratObject(counts = H34, project = \"H34\", min.cells = 3, min.features = 100)\n",
    "H34\n",
    "H35 <- CreateSeuratObject(counts = H35, project = \"H35\", min.cells = 3, min.features = 100)\n",
    "H35\n",
    "H36 <- CreateSeuratObject(counts = H36, project = \"H36\", min.cells = 3, min.features = 100)\n",
    "H36\n",
    "H38 <- CreateSeuratObject(counts = H38, project = \"H38\", min.cells = 3, min.features = 100)\n",
    "H38\n",
    "H39 <- CreateSeuratObject(counts = H39, project = \"H39\", min.cells = 3, min.features = 100)\n",
    "H39\n",
    "H41 <- CreateSeuratObject(counts = H41, project = \"H41\", min.cells = 3, min.features = 100)\n",
    "H41"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076af142-12ef-49d0-b78b-6e2d984725fa",
   "metadata": {},
   "source": [
    "# Preprocess samples individually to remove doublets -----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33ed63fa-6d40-4cb9-8c1f-70d762ddcd1b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'doubletFinder'</li><li>'find.pK'</li><li>'modelHomotypic'</li><li>'paramSweep'</li><li>'pbmc_small'</li><li>'summarizeSweep'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'doubletFinder'\n",
       "\\item 'find.pK'\n",
       "\\item 'modelHomotypic'\n",
       "\\item 'paramSweep'\n",
       "\\item 'pbmc\\_small'\n",
       "\\item 'summarizeSweep'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'doubletFinder'\n",
       "2. 'find.pK'\n",
       "3. 'modelHomotypic'\n",
       "4. 'paramSweep'\n",
       "5. 'pbmc_small'\n",
       "6. 'summarizeSweep'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"doubletFinder\"  \"find.pK\"        \"modelHomotypic\" \"paramSweep\"    \n",
       "[5] \"pbmc_small\"     \"summarizeSweep\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## error \"paramSweep_v3\"   ls(\"package:DoubletFinder\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a5d5239-85d8-4ac4-aa5d-1bc4ea53a6ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'bimodality_coefficient'</li><li>'doubletFinder'</li><li>'doubletFinder_v3'</li><li>'find.pK'</li><li>'kurtosis'</li><li>'modelHomotypic'</li><li>'parallel_paramSweep_v3'</li><li>'paramSweep'</li><li>'paramSweep_v3'</li><li>'skewness'</li><li>'summarizeSweep'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'bimodality\\_coefficient'\n",
       "\\item 'doubletFinder'\n",
       "\\item 'doubletFinder\\_v3'\n",
       "\\item 'find.pK'\n",
       "\\item 'kurtosis'\n",
       "\\item 'modelHomotypic'\n",
       "\\item 'parallel\\_paramSweep\\_v3'\n",
       "\\item 'paramSweep'\n",
       "\\item 'paramSweep\\_v3'\n",
       "\\item 'skewness'\n",
       "\\item 'summarizeSweep'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'bimodality_coefficient'\n",
       "2. 'doubletFinder'\n",
       "3. 'doubletFinder_v3'\n",
       "4. 'find.pK'\n",
       "5. 'kurtosis'\n",
       "6. 'modelHomotypic'\n",
       "7. 'parallel_paramSweep_v3'\n",
       "8. 'paramSweep'\n",
       "9. 'paramSweep_v3'\n",
       "10. 'skewness'\n",
       "11. 'summarizeSweep'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"bimodality_coefficient\" \"doubletFinder\"          \"doubletFinder_v3\"      \n",
       " [4] \"find.pK\"                \"kurtosis\"               \"modelHomotypic\"        \n",
       " [7] \"parallel_paramSweep_v3\" \"paramSweep\"             \"paramSweep_v3\"         \n",
       "[10] \"skewness\"               \"summarizeSweep\"        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls(\"package:DoubletFinder\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32c6b918-63c0-4f35-b73f-399906510f01",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "paramSweep_v3 <- function (seu, PCs = 1:10, sct = FALSE, num.cores = 1) \n",
    "{\n",
    "  require(Seurat)\n",
    "  require(fields)\n",
    "  pK <- c(5e-04, 0.001, 0.005, seq(0.01, 0.3, by = 0.01))\n",
    "  pN <- seq(0.05, 0.3, by = 0.05)\n",
    "  min.cells <- round(nrow(seu@meta.data)/(1 - 0.05) - nrow(seu@meta.data))\n",
    "  pK.test <- round(pK * min.cells)\n",
    "  pK <- pK[which(pK.test >= 1)]\n",
    "  orig.commands <- seu@commands\n",
    "  if (nrow(seu@meta.data) > 10000) {\n",
    "    real.cells <- rownames(seu@meta.data)[sample(1:nrow(seu@meta.data), \n",
    "                                                 10000, replace = FALSE)]\n",
    "\n",
    "    data <- seu@assays$RNA@layers$counts ## M\n",
    "    colnames(data) <- colnames(seu) ## M\n",
    "    rownames(data) <- rownames(seu) ## M\n",
    "    data <- data[ ,real.cells] ## M\n",
    "    \n",
    "    n.real.cells <- ncol(data)\n",
    "  }\n",
    "  if (nrow(seu@meta.data) <= 10000) {\n",
    "    real.cells <- rownames(seu@meta.data)\n",
    "    data <- seu@assays$RNA@layers$counts ## M\n",
    "    colnames(data) <- colnames(seu) ## M\n",
    "    rownames(data) <- rownames(seu) ## M\n",
    "    n.real.cells <- ncol(data)\n",
    "  }\n",
    "  if (num.cores > 1) {\n",
    "    require(parallel)\n",
    "    cl <- makeCluster(num.cores)\n",
    "    output2 <- mclapply(as.list(1:length(pN)), FUN = parallel_paramSweep_v3, \n",
    "                        n.real.cells, real.cells, pK, pN, data, orig.commands, \n",
    "                        PCs, sct, mc.cores = num.cores)\n",
    "    stopCluster(cl)\n",
    "  }\n",
    "  else {\n",
    "    output2 <- lapply(as.list(1:length(pN)), FUN = parallel_paramSweep_v3, \n",
    "                      n.real.cells, real.cells, pK, pN, data, orig.commands, \n",
    "                      PCs, sct)\n",
    "  }\n",
    "  sweep.res.list <- list()\n",
    "  list.ind <- 0\n",
    "  for (i in 1:length(output2)) {\n",
    "    for (j in 1:length(output2[[i]])) {\n",
    "      list.ind <- list.ind + 1\n",
    "      sweep.res.list[[list.ind]] <- output2[[i]][[j]]\n",
    "    }\n",
    "  }\n",
    "  name.vec <- NULL\n",
    "  for (j in 1:length(pN)) {\n",
    "    name.vec <- c(name.vec, paste(\"pN\", pN[j], \"pK\", pK, \n",
    "                                  sep = \"_\"))\n",
    "  }\n",
    "  names(sweep.res.list) <- name.vec\n",
    "  return(sweep.res.list)\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "parallel_paramSweep_v3 <- function (n, n.real.cells, real.cells, pK, pN, data, orig.commands, \n",
    "                                    PCs, sct) \n",
    "{\n",
    "  sweep.res.list = list()\n",
    "  list.ind = 0\n",
    "  print(paste(\"Creating artificial doublets for pN = \", pN[n] * \n",
    "                100, \"%\", sep = \"\"))\n",
    "  n_doublets <- round(n.real.cells/(1 - pN[n]) - n.real.cells)\n",
    "  real.cells1 <- sample(real.cells, n_doublets, replace = TRUE)\n",
    "  real.cells2 <- sample(real.cells, n_doublets, replace = TRUE)\n",
    "  doublets <- (data[, real.cells1] + data[, real.cells2])/2\n",
    "  colnames(doublets) <- paste(\"X\", 1:n_doublets, sep = \"\")\n",
    "  data_wdoublets <- cbind(data, doublets)\n",
    "  if (sct == FALSE) {\n",
    "    print(\"Creating Seurat object...\")\n",
    "    seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)\n",
    "    print(\"Normalizing Seurat object...\")\n",
    "    seu_wdoublets <- NormalizeData(seu_wdoublets, normalization.method = orig.commands$NormalizeData.RNA@params$normalization.method, \n",
    "                                   scale.factor = orig.commands$NormalizeData.RNA@params$scale.factor, \n",
    "                                   margin = orig.commands$NormalizeData.RNA@params$margin)\n",
    "    print(\"Finding variable genes...\")\n",
    "    seu_wdoublets <- FindVariableFeatures(seu_wdoublets, \n",
    "                                          selection.method = orig.commands$FindVariableFeatures.RNA$selection.method, \n",
    "                                          loess.span = orig.commands$FindVariableFeatures.RNA$loess.span, \n",
    "                                          clip.max = orig.commands$FindVariableFeatures.RNA$clip.max, \n",
    "                                          mean.function = orig.commands$FindVariableFeatures.RNA$mean.function, \n",
    "                                          dispersion.function = orig.commands$FindVariableFeatures.RNA$dispersion.function, \n",
    "                                          num.bin = orig.commands$FindVariableFeatures.RNA$num.bin, \n",
    "                                          binning.method = orig.commands$FindVariableFeatures.RNA$binning.method, \n",
    "                                          nfeatures = orig.commands$FindVariableFeatures.RNA$nfeatures, \n",
    "                                          mean.cutoff = orig.commands$FindVariableFeatures.RNA$mean.cutoff, \n",
    "                                          dispersion.cutoff = orig.commands$FindVariableFeatures.RNA$dispersion.cutoff)\n",
    "    print(\"Scaling data...\")\n",
    "    seu_wdoublets <- ScaleData(seu_wdoublets, features = orig.commands$ScaleData.RNA$features, \n",
    "                               model.use = orig.commands$ScaleData.RNA$model.use, \n",
    "                               do.scale = orig.commands$ScaleData.RNA$do.scale, \n",
    "                               do.center = orig.commands$ScaleData.RNA$do.center, \n",
    "                               scale.max = orig.commands$ScaleData.RNA$scale.max, \n",
    "                               block.size = orig.commands$ScaleData.RNA$block.size, \n",
    "                               min.cells.to.block = orig.commands$ScaleData.RNA$min.cells.to.block)\n",
    "    print(\"Running PCA...\")\n",
    "    seu_wdoublets <- RunPCA(seu_wdoublets, features = orig.commands$ScaleData.RNA$features, \n",
    "                            npcs = length(PCs), rev.pca = orig.commands$RunPCA.RNA$rev.pca, \n",
    "                            weight.by.var = orig.commands$RunPCA.RNA$weight.by.var, \n",
    "                            verbose = FALSE)\n",
    "  }\n",
    "  if (sct == TRUE) {\n",
    "    require(sctransform)\n",
    "    print(\"Creating Seurat object...\")\n",
    "    seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)\n",
    "    print(\"Running SCTransform...\")\n",
    "    seu_wdoublets <- SCTransform(seu_wdoublets)\n",
    "    print(\"Running PCA...\")\n",
    "    seu_wdoublets <- RunPCA(seu_wdoublets, npcs = length(PCs))\n",
    "  }\n",
    "  print(\"Calculating PC distance matrix...\")\n",
    "  nCells <- nrow(seu_wdoublets@meta.data)\n",
    "  pca.coord <- seu_wdoublets@reductions$pca@cell.embeddings[, \n",
    "                                                            PCs]\n",
    "  rm(seu_wdoublets)\n",
    "  gc()\n",
    "  dist.mat <- fields::rdist(pca.coord)[, 1:n.real.cells]\n",
    "  print(\"Defining neighborhoods...\")\n",
    "  for (i in 1:n.real.cells) {\n",
    "    dist.mat[, i] <- order(dist.mat[, i])\n",
    "  }\n",
    "  ind <- round(nCells * max(pK)) + 5\n",
    "  dist.mat <- dist.mat[1:ind, ]\n",
    "  print(\"Computing pANN across all pK...\")\n",
    "  for (k in 1:length(pK)) {\n",
    "    print(paste(\"pK = \", pK[k], \"...\", sep = \"\"))\n",
    "    pk.temp <- round(nCells * pK[k])\n",
    "    pANN <- as.data.frame(matrix(0L, nrow = n.real.cells, \n",
    "                                 ncol = 1))\n",
    "    colnames(pANN) <- \"pANN\"\n",
    "    rownames(pANN) <- real.cells\n",
    "    list.ind <- list.ind + 1\n",
    "    for (i in 1:n.real.cells) {\n",
    "      neighbors <- dist.mat[2:(pk.temp + 1), i]\n",
    "      pANN$pANN[i] <- length(which(neighbors > n.real.cells))/pk.temp\n",
    "    }\n",
    "    sweep.res.list[[list.ind]] <- pANN\n",
    "  }\n",
    "  return(sweep.res.list)\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "doubletFinder_v3 <- function (seu, PCs, pN = 0.25, pK, nExp, reuse.pANN = FALSE, \n",
    "          sct = FALSE, annotations = NULL) \n",
    "{\n",
    "  require(Seurat)\n",
    "  require(fields)\n",
    "  require(KernSmooth)\n",
    "  if (reuse.pANN != FALSE) {\n",
    "    pANN.old <- seu@meta.data[, reuse.pANN]\n",
    "    classifications <- rep(\"Singlet\", length(pANN.old))\n",
    "    classifications[order(pANN.old, decreasing = TRUE)[1:nExp]] <- \"Doublet\"\n",
    "    seu@meta.data[, paste(\"DF.classifications\", pN, pK, nExp, \n",
    "                          sep = \"_\")] <- classifications\n",
    "    return(seu)\n",
    "  }\n",
    "  if (reuse.pANN == FALSE) {\n",
    "    real.cells <- rownames(seu@meta.data)\n",
    "    \n",
    "    data <- seu@assays$RNA@layers$counts ## M\n",
    "    colnames(data) <- colnames(seu) ## M\n",
    "    rownames(data) <- rownames(seu) ## M\n",
    "    data <- data[, real.cells] ## M\n",
    "    \n",
    "    n_real.cells <- length(real.cells)\n",
    "    n_doublets <- round(n_real.cells/(1 - pN) - n_real.cells)\n",
    "    print(paste(\"Creating\", n_doublets, \"artificial doublets...\", \n",
    "                sep = \" \"))\n",
    "    real.cells1 <- sample(real.cells, n_doublets, replace = TRUE)\n",
    "    real.cells2 <- sample(real.cells, n_doublets, replace = TRUE)\n",
    "    doublets <- (data[, real.cells1] + data[, real.cells2])/2\n",
    "    colnames(doublets) <- paste(\"X\", 1:n_doublets, sep = \"\")\n",
    "    data_wdoublets <- cbind(data, doublets)\n",
    "    if (!is.null(annotations)) {\n",
    "      stopifnot(typeof(annotations) == \"character\")\n",
    "      stopifnot(length(annotations) == length(Cells(seu)))\n",
    "      stopifnot(!any(is.na(annotations)))\n",
    "      annotations <- factor(annotations)\n",
    "      names(annotations) <- Cells(seu)\n",
    "      doublet_types1 <- annotations[real.cells1]\n",
    "      doublet_types2 <- annotations[real.cells2]\n",
    "    }\n",
    "    orig.commands <- seu@commands\n",
    "    if (sct == FALSE) {\n",
    "      print(\"Creating Seurat object...\")\n",
    "      seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)\n",
    "      print(\"Normalizing Seurat object...\")\n",
    "      seu_wdoublets <- NormalizeData(seu_wdoublets, normalization.method = orig.commands$NormalizeData.RNA@params$normalization.method, \n",
    "                                     scale.factor = orig.commands$NormalizeData.RNA@params$scale.factor, \n",
    "                                     margin = orig.commands$NormalizeData.RNA@params$margin)\n",
    "      print(\"Finding variable genes...\")\n",
    "      seu_wdoublets <- FindVariableFeatures(seu_wdoublets, \n",
    "                                            selection.method = orig.commands$FindVariableFeatures.RNA$selection.method, \n",
    "                                            loess.span = orig.commands$FindVariableFeatures.RNA$loess.span, \n",
    "                                            clip.max = orig.commands$FindVariableFeatures.RNA$clip.max, \n",
    "                                            mean.function = orig.commands$FindVariableFeatures.RNA$mean.function, \n",
    "                                            dispersion.function = orig.commands$FindVariableFeatures.RNA$dispersion.function, \n",
    "                                            num.bin = orig.commands$FindVariableFeatures.RNA$num.bin, \n",
    "                                            binning.method = orig.commands$FindVariableFeatures.RNA$binning.method, \n",
    "                                            nfeatures = orig.commands$FindVariableFeatures.RNA$nfeatures, \n",
    "                                            mean.cutoff = orig.commands$FindVariableFeatures.RNA$mean.cutoff, \n",
    "                                            dispersion.cutoff = orig.commands$FindVariableFeatures.RNA$dispersion.cutoff)\n",
    "      print(\"Scaling data...\")\n",
    "      seu_wdoublets <- ScaleData(seu_wdoublets, features = orig.commands$ScaleData.RNA$features, \n",
    "                                 model.use = orig.commands$ScaleData.RNA$model.use, \n",
    "                                 do.scale = orig.commands$ScaleData.RNA$do.scale, \n",
    "                                 do.center = orig.commands$ScaleData.RNA$do.center, \n",
    "                                 scale.max = orig.commands$ScaleData.RNA$scale.max, \n",
    "                                 block.size = orig.commands$ScaleData.RNA$block.size, \n",
    "                                 min.cells.to.block = orig.commands$ScaleData.RNA$min.cells.to.block)\n",
    "      print(\"Running PCA...\")\n",
    "      seu_wdoublets <- RunPCA(seu_wdoublets, features = orig.commands$ScaleData.RNA$features, \n",
    "                              npcs = length(PCs), rev.pca = orig.commands$RunPCA.RNA$rev.pca, \n",
    "                              weight.by.var = orig.commands$RunPCA.RNA$weight.by.var, \n",
    "                              verbose = FALSE)\n",
    "      pca.coord <- seu_wdoublets@reductions$pca@cell.embeddings[, \n",
    "                                                                PCs]\n",
    "      cell.names <- rownames(seu_wdoublets@meta.data)\n",
    "      nCells <- length(cell.names)\n",
    "      rm(seu_wdoublets)\n",
    "      gc()\n",
    "    }\n",
    "    if (sct == TRUE) {\n",
    "      require(sctransform)\n",
    "      print(\"Creating Seurat object...\")\n",
    "      seu_wdoublets <- CreateSeuratObject(counts = data_wdoublets)\n",
    "      print(\"Running SCTransform...\")\n",
    "      seu_wdoublets <- SCTransform(seu_wdoublets)\n",
    "      print(\"Running PCA...\")\n",
    "      seu_wdoublets <- RunPCA(seu_wdoublets, npcs = length(PCs))\n",
    "      pca.coord <- seu_wdoublets@reductions$pca@cell.embeddings[, \n",
    "                                                                PCs]\n",
    "      cell.names <- rownames(seu_wdoublets@meta.data)\n",
    "      nCells <- length(cell.names)\n",
    "      rm(seu_wdoublets)\n",
    "      gc()\n",
    "    }\n",
    "    print(\"Calculating PC distance matrix...\")\n",
    "    dist.mat <- fields::rdist(pca.coord)\n",
    "    print(\"Computing pANN...\")\n",
    "    pANN <- as.data.frame(matrix(0L, nrow = n_real.cells, \n",
    "                                 ncol = 1))\n",
    "    if (!is.null(annotations)) {\n",
    "      neighbor_types <- as.data.frame(matrix(0L, nrow = n_real.cells, \n",
    "                                             ncol = length(levels(doublet_types1))))\n",
    "    }\n",
    "    rownames(pANN) <- real.cells\n",
    "    colnames(pANN) <- \"pANN\"\n",
    "    k <- round(nCells * pK)\n",
    "    for (i in 1:n_real.cells) {\n",
    "      neighbors <- order(dist.mat[, i])\n",
    "      neighbors <- neighbors[2:(k + 1)]\n",
    "      pANN$pANN[i] <- length(which(neighbors > n_real.cells))/k\n",
    "      if (!is.null(annotations)) {\n",
    "        for (ct in unique(annotations)) {\n",
    "          neighbors_that_are_doublets = neighbors[neighbors > \n",
    "                                                    n_real.cells]\n",
    "          if (length(neighbors_that_are_doublets) > 0) {\n",
    "            neighbor_types[i, ] <- table(doublet_types1[neighbors_that_are_doublets - \n",
    "                                                          n_real.cells]) + table(doublet_types2[neighbors_that_are_doublets - \n",
    "                                                                                                  n_real.cells])\n",
    "            neighbor_types[i, ] <- neighbor_types[i, \n",
    "            ]/sum(neighbor_types[i, ])\n",
    "          }\n",
    "          else {\n",
    "            neighbor_types[i, ] <- NA\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "    print(\"Classifying doublets..\")\n",
    "    classifications <- rep(\"Singlet\", n_real.cells)\n",
    "    classifications[order(pANN$pANN[1:n_real.cells], decreasing = TRUE)[1:nExp]] <- \"Doublet\"\n",
    "    seu@meta.data[, paste(\"pANN\", pN, pK, nExp, sep = \"_\")] <- pANN[rownames(seu@meta.data), \n",
    "                                                                    1]\n",
    "    seu@meta.data[, paste(\"DF.classifications\", pN, pK, nExp, \n",
    "                          sep = \"_\")] <- classifications\n",
    "    if (!is.null(annotations)) {\n",
    "      colnames(neighbor_types) = levels(doublet_types1)\n",
    "      for (ct in levels(doublet_types1)) {\n",
    "        seu@meta.data[, paste(\"DF.doublet.contributors\", \n",
    "                              pN, pK, nExp, ct, sep = \"_\")] <- neighbor_types[, \n",
    "                                                                              ct]\n",
    "      }\n",
    "    }\n",
    "    return(seu)\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c65fd492-c0dc-4a28-b6f8-dcfa59043179",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  AHSP, BIRC3, HBB, PRDX2, TMEM156, HBA1, AREG, STRBP, IGLV3-1, IGHG1 \n",
      "\t   HBA2, PDK1, PCLAF, BMP6, HMGA1, IFNG-AS1, BLNK, HIST1H1D, CA1, IRF4 \n",
      "\t   RGS1, AC007569.1, DNAAF1, SAMSN1, IGHA1, BLVRB, ITGA4, DPEP1, ZBP1, KLF1 \n",
      "Negative:  LIFR, OSMR, AP002518.2, SNED1, COL5A2, MYO1E, TMEM108, SATB2, MAGI1, MYO10 \n",
      "\t   PPFIA2, PDZRN4, GLIS3, PSD3, NCAM2, COL1A2, SLIT2, SLIT3, ANTXR1, EYA1 \n",
      "\t   NAV3, CRISPLD2, RUNX2, PCSK5, PLD1, ADAMTS9, COL4A1, AL138828.1, CNKSR3, ENPP2 \n",
      "PC_ 2 \n",
      "Positive:  LDB2, ADGRL4, PTPRB, EMCN, VWF, MCTP1, FLT1, ST6GALNAC3, ANO2, PLEKHG1 \n",
      "\t   TEK, SHANK3, ERG, CYYR1, CALCRL, RAPGEF5, BTNL9, CNTNAP3B, TM4SF1, CDH5 \n",
      "\t   STOX2, THBD, NOSTRIN, RNASE1, CD93, MECOM, CLEC14A, PCAT19, PODXL, RAMP3 \n",
      "Negative:  COL1A2, SERPINF1, SATB2, RUNX2, PDZRN4, TMEM108, FGF7, PPFIA2, COL3A1, ANTXR1 \n",
      "\t   TNC, EYA1, FAT3, PAPPA, NCAM2, ITGBL1, SLIT2, PID1, TF, COL5A2 \n",
      "\t   RETREG1, SLC1A3, COL1A1, PCDH9, ID4, PRKAR2B, TNFAIP6, NCAM1, COL6A3, PTPRD \n",
      "PC_ 3 \n",
      "Positive:  EIF2AK3, BIRC3, RAB30, TXNDC11, PDK1, BMP6, BLNK, TMEM156, IFNG-AS1, CELF2 \n",
      "\t   TBC1D9, TEX14, AC007569.1, IRF4, CHST11, OSBPL3, FAAH2, STRBP, AREG, ALCAM \n",
      "\t   AC104134.1, DENND1B, OTUD1, IKZF3, ZNF215, ZNF331, ZBP1, USP3-AS1, RGS1, TXLNB \n",
      "Negative:  HBB, AHSP, HBA1, CA1, BLVRB, PRDX2, HBA2, PCLAF, KLF1, HMGA1 \n",
      "\t   HMGB2, FAM178B, STMN1, MYL4, TYMS, AKR1C3, KCNH2, TOP2A, HIST1H1B, HEMGN \n",
      "\t   HIST1H4C, BIRC5, PTTG1, GYPA, GATA1, DLGAP5, CKS2, ALAS2, CCNB2, AC084033.3 \n",
      "PC_ 4 \n",
      "Positive:  NCAM2, EYA1, TNFAIP6, ADAMTSL3, NLGN1, DCLK1, VCAN-AS1, TMEM108, MIR646HG, RETREG1 \n",
      "\t   PAPPA, IGFBP2, CNTNAP2, SLC1A3, IGFBP4, COL14A1, ROR2, CCBE1, ADAM28, DMD \n",
      "\t   GLIS3, COL3A1, PCSK6, PID1, CFAP69, RGS6, POU6F2, PDE1A, AC120193.1, BNC2 \n",
      "Negative:  WIF1, COL13A1, IBSP, PTPRD, MMP16, SRGAP3, CADM1, CPE, NCAM1, BGLAP \n",
      "\t   INSC, CHAD, CDH15, SFRP4, OMD, SPP1, SLC44A5, WDR72, LRP4, PTH1R \n",
      "\t   PDGFD, GAS7, FRMD4B, BMP3, LRP4-AS1, ANO5, IFITM5, SERPINE2, LMO7, COLEC12 \n",
      "PC_ 5 \n",
      "Positive:  MRVI1, RGS5, CASQ2, MYH11, LDB3, DGKB, RERGL, KCNAB1, RCAN2, PDE3A \n",
      "\t   NDUFA4L2, PHLDA2, NMNAT2, MCAM, EBF2, INPP4B, ERBB4, CTNNA3, LBH, COX4I2 \n",
      "\t   LMOD1, CCDC3, SCN3A, CPM, COL4A5, GUCY1A1, CNN1, MYOCD, GRIN2A, PRKG1 \n",
      "Negative:  RUNX2, NCAM1, SLIT2, CNKSR3, MYO10, LIFR, LRP4, EFNA5, ITGBL1, SULF1 \n",
      "\t   WIF1, ENPP2, UST, AL138828.1, PTPRD, MMP16, FAT3, COLEC12, ABLIM1, FRMD4B \n",
      "\t   SATB2, ANKH, LRRC1, INSC, IBSP, SRGAP3, WDR72, GAS7, OMD, COL1A2 \n",
      "\n",
      "19:20:03 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:20:03 Read 6910 rows and found 30 numeric columns\n",
      "\n",
      "19:20:03 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:20:03 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
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      "[----|----|----|----|----|----|----|----|----|----|\n",
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      "|\n",
      "\n",
      "19:20:04 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a4d9c97a3\n",
      "\n",
      "19:20:04 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:20:06 Annoy recall = 100%\n",
      "\n",
      "19:20:06 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:20:08 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:20:08 Commencing optimization for 500 epochs, with 279760 positive edges\n",
      "\n",
      "19:20:08 Using rng type: pcg\n",
      "\n",
      "19:20:16 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:20:16 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:20:16 Read 6910 rows and found 50 numeric columns\n",
      "\n",
      "19:20:16 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:20:16 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
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      "|\n",
      "\n",
      "19:20:16 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a23bfd692\n",
      "\n",
      "19:20:16 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:20:18 Annoy recall = 100%\n",
      "\n",
      "19:20:19 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:20:20 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:20:20 Commencing optimization for 500 epochs, with 281422 positive edges\n",
      "\n",
      "19:20:20 Using rng type: pcg\n",
      "\n",
      "19:20:28 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 6910\n",
      "Number of edges: 237408\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.8874\n",
      "Number of communities: 24\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "NULL\n",
      "[1] \"Creating 2303 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  ARHGAP15, IQGAP2, HMGA1, PTPRC, BCL11A, LCP1, GNA15, PRKCB, SRGN, HLA-DRA \n",
      "\t   STMN1, PLAC8, SPN, JARID2, MCTP2, ITGA4, AIF1, AREG, P2RY8, CDK6 \n",
      "\t   PRSS57, MYB, HIST1H1D, PCLAF, MBP, KCNQ5, KCNAB2, CORO1A, CD69, FLT3 \n",
      "Negative:  COL1A2, MGP, FN1, COL3A1, KAZN, COL5A2, CCDC80, COL14A1, SPARC, COL1A1 \n",
      "\t   CLU, COL6A3, ABI3BP, HTRA1, TIMP1, MT2A, TNC, SERPINF1, VCAN, FBLN1 \n",
      "\t   ISLR, WISP2, IGFBP4, EFEMP1, SOX5, TIMP3, CYR61, EDIL3, ERRFI1, MYL9 \n",
      "PC_ 2 \n",
      "Positive:  EMCN, ADGRL4, VWF, PTPRB, FLT1, LDB2, CALCRL, RNASE1, CYYR1, TM4SF1 \n",
      "\t   RAMP3, PLVAP, ANO2, TEK, CXorf36, PREX2, IL33, PODXL, SLCO2A1, NOSTRIN \n",
      "\t   SOX18, PLAT, PCDH17, RAPGEF4, THSD7A, PLEKHG1, LMCD1, NUAK1, ST6GALNAC3, TM4SF18 \n",
      "Negative:  CD44, COL1A2, XYLT1, COL3A1, COL14A1, COL6A3, PLA2G2A, OGN, MEG3, VCAN \n",
      "\t   WISP2, GFPT2, EFEMP1, ABI3BP, DPT, COL1A1, COL5A2, GEM, TNFAIP6, ISLR \n",
      "\t   CFD, PRELP, ANK2, ZNF385B, HAS1, GAS1, IGF1, GPC6, SEMA3C, FGF10 \n",
      "PC_ 3 \n",
      "Positive:  IGKC, NCAM1, WIF1, SATB2, IBSP, FAT3, DERL3, BGLAP, FBXO32, MZB1 \n",
      "\t   SFRP4, CD27, IGHG1, FCRL5, SLAMF7, DAPK2, IGLC2, TNFRSF17, CHAD, SPAG4 \n",
      "\t   LMO7, SPP1, SLIT2, CPE, ITGA8, OMD, CADM1, TNFRSF13B, IGHA1, MMP16 \n",
      "Negative:  ZNF385D, TNXB, CREB5, IGFBP4, MEG3, TUBB, CRTAC1, PLA2G2A, GFPT2, ZNF385B \n",
      "\t   AQP1, EPB41L3, OGN, MEDAG, LDB2, CALCRL, HAS1, CCDC80, COL15A1, SMC4 \n",
      "\t   LRP1B, HLA-DRB1, ABI3BP, HTRA1, EFEMP1, TUBA1B, VWF, RCAN1, TYMS, LMCD1 \n",
      "PC_ 4 \n",
      "Positive:  SATB2, HIST1H4C, PCLAF, CA1, FAT3, PRKAR2B, NCAM1, BLVRB, KLF1, WIF1 \n",
      "\t   TYMS, DUT, AHSP, FAM178B, BMP5, PPFIA2, HBD, SLIT2, IBSP, HIST1H1B \n",
      "\t   CPE, TPM2, CENPU, STMN1, APOE, KCNH2, HBB, PRKG1, LMO7, TUBA1B \n",
      "Negative:  BASP1, CD44, SAMSN1, CD55, S100A9, TREM1, BCL2A1, PIK3R5, TYROBP, FAM49A \n",
      "\t   RGCC, RBM47, S100A8, MNDA, CREB5, PLA2G2A, IER3, PLAUR, PTGS2, C5AR1 \n",
      "\t   SRGN, FCER1G, LCP1, PTPRC, CXCL8, FPR1, NFKB1, HAS1, MEG3, ANPEP \n",
      "PC_ 5 \n",
      "Positive:  CXCL8, LYZ, TYROBP, S100A9, PLAUR, MNDA, PIK3R5, BCL2A1, S100A8, SIPA1L1 \n",
      "\t   LST1, LCP1, AC020656.1, DOCK5, FCER1G, IL1B, C5AR1, PTPRC, LCP2, NLRP3 \n",
      "\t   CSF3R, MXD1, ITGAX, JARID2, OLR1, NRXN3, FPR1, BMP5, SLC43A2, CSTA \n",
      "Negative:  SSR4, MZB1, DERL3, FKBP11, SEC11C, SLAMF7, FCRL5, CD27, TNFRSF17, SPAG4 \n",
      "\t   POU2AF1, TNFRSF13B, FAM92B, DUSP5, IFNG-AS1, CYTOR, IGLV3-1, DNAAF1, HERPUD1, PAIP2B \n",
      "\t   TMEM156, JCHAIN, JSRP1, RASSF6, ZNF215, FAAH2, IGHG1, CD79A, PRDX2, BLNK \n",
      "\n",
      "19:22:56 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:22:56 Read 7692 rows and found 30 numeric columns\n",
      "\n",
      "19:22:56 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:22:56 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:22:57 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a5060280c\n",
      "\n",
      "19:22:57 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:22:58 Annoy recall = 100%\n",
      "\n",
      "19:22:59 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:23:01 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:23:01 Commencing optimization for 500 epochs, with 315612 positive edges\n",
      "\n",
      "19:23:01 Using rng type: pcg\n",
      "\n",
      "19:23:10 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:23:10 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:23:10 Read 7692 rows and found 50 numeric columns\n",
      "\n",
      "19:23:10 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:23:10 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:23:10 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a410f277d\n",
      "\n",
      "19:23:10 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:23:12 Annoy recall = 100%\n",
      "\n",
      "19:23:13 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:23:14 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:23:15 Commencing optimization for 500 epochs, with 314828 positive edges\n",
      "\n",
      "19:23:15 Using rng type: pcg\n",
      "\n",
      "19:23:23 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 7692\n",
      "Number of edges: 259784\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9164\n",
      "Number of communities: 27\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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whCmfbyfizkaxhFf/e7bz7/war54xud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chajWKx6/O5F8S2MNutepDIKdyPqc1kxW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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 2564 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  COL1A2, LUM, SERPINF1, MGP, CFD, COL3A1, PCOLCE, COL5A2, C1R, APOE \n",
      "\t   FN1, FSTL1, IGFBP5, FGF7, COL1A1, COL6A3, EPHA3, TAGLN, CCDC80, SPARC \n",
      "\t   BGN, PPFIA2, VCAN, ABI3BP, ID4, ERRFI1, IGFBP6, CHSY3, SATB2, AEBP1 \n",
      "Negative:  ARHGAP15, PTPRC, IQGAP2, SAMSN1, LCP1, MCTP2, P2RY8, PRKCB, RHOH, CYTIP \n",
      "\t   INPP5D, ITGA4, VAMP8, AREG, JARID2, RAB11FIP1, AIF1, PLEK, SYK, CD74 \n",
      "\t   P2RX1, PLAC8, GNA15, BCL11A, CD52, PIK3R5, HMGA1, TBXAS1, SPN, HLA-DQB1 \n",
      "PC_ 2 \n",
      "Positive:  CD44, CFD, SERPINF1, ARHGAP15, LUM, COL1A2, FAM107B, CELF2, BASP1, SAMSN1 \n",
      "\t   PTPRC, LCP1, DOCK10, PCOLCE, COL3A1, MCTP2, APOE, XYLT1, C1R, PLAUR \n",
      "\t   PRKCB, TNFAIP6, PLEK, CYTIP, FGF7, PTPRD, IQGAP2, PIK3R5, PID1, ACSL1 \n",
      "Negative:  EMCN, ADGRL4, LDB2, VWF, FABP4, FLT1, RNASE1, PALMD, CALCRL, IFI27 \n",
      "\t   PTPRB, TM4SF1, DNASE1L3, BTNL9, TEK, RBP7, PLAT, CD36, ANO2, CHRM3 \n",
      "\t   RAMP3, NOSTRIN, ST6GALNAC3, PLVAP, PLEKHG1, TM4SF18, AQP1, STOX2, SLC9A3R2, CAVIN2 \n",
      "PC_ 3 \n",
      "Positive:  H2AFY, GNA15, AIF1, PRSS57, PCLAF, EREG, HMGA1, ENO1, TYMS, RNF130 \n",
      "\t   HLA-DRA, STMN1, EMILIN2, HIST1H1B, MYB, CENPF, TUBB, HMGB2, CERS6, DIAPH3 \n",
      "\t   SERPINB1, ATP2C1, HLA-DRB1, MKI67, MAML3, SMIM24, CDT1, CENPU, TOP2A, TUBA1B \n",
      "Negative:  MZB1, DERL3, FCRL5, SLAMF7, FKBP11, SEC11C, TNFRSF17, CD27, SPAG4, POU2AF1 \n",
      "\t   JSRP1, IFNG-AS1, TNFRSF13B, SLAMF1, HERPUD1, IGHA1, TMEM156, BLNK, IGLC2, PRDM1 \n",
      "\t   FAAH2, DUSP5, RAB30, IGHG1, IGHG3, IGHA2, AC092546.1, AQP3, RASSF6, DCC \n",
      "PC_ 4 \n",
      "Positive:  PCLAF, TYMS, CDK6, HIST1H1B, IGLL1, PRSS57, MYB, STMN1, HMGA1, KIAA1211 \n",
      "\t   CDT1, LDHB, HIST1H1D, BCL11A, AC084033.3, CENPF, AC002454.1, KCNQ5, DIAPH3, ANKRD28 \n",
      "\t   FHIT, HIST1H4C, HSPD1, ORC6, TUBA1B, HMGB3, FAM30A, CABLES1, SMIM24, BIRC5 \n",
      "Negative:  S100A9, S100A8, BCL2A1, S100A12, MNDA, TYROBP, C5AR1, FPR1, SLC11A1, ITGAX \n",
      "\t   FCER1G, AQP9, PIK3R5, BASP1, FCN1, LCP2, PADI4, G0S2, LST1, CXCL8 \n",
      "\t   MMP9, LUCAT1, LCP1, TREM1, ACSL1, LYZ, CSF3R, SAMSN1, MGAM, IL1R2 \n",
      "PC_ 5 \n",
      "Positive:  LAMA2, NEGR1, IGF1, C3, MEG3, FAM49A, COL6A3, DCLK1, CILP, VCAN \n",
      "\t   MFAP4, SOX5, FBLN5, MYOC, TNXB, NOVA1, PELI1, APOD, MFAP5, F3 \n",
      "\t   COL15A1, CARMIL1, NFKB1, LRRTM4, CD44, CXCL14, SERPINB9, KCND2, ABI3BP, HMCN2 \n",
      "Negative:  IBSP, SPP1, CPE, CHAD, COL13A1, BGLAP, PTH1R, CADM1, IFITM5, BMP3 \n",
      "\t   SFRP4, TNC, MMP16, SATB2, PTPRD, SLC44A5, OMD, ANO5, HS3ST4, PTPRZ1 \n",
      "\t   PDGFD, AC009055.1, RAMP1, SDK2, FAT1, CDH2, ENPP1, BGN, BAMBI, SULF1 \n",
      "\n",
      "19:26:28 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:26:28 Read 11589 rows and found 30 numeric columns\n",
      "\n",
      "19:26:28 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:26:28 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:26:29 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a4c44bd3e\n",
      "\n",
      "19:26:29 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:26:32 Annoy recall = 100%\n",
      "\n",
      "19:26:32 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:26:34 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:26:35 Commencing optimization for 200 epochs, with 483678 positive edges\n",
      "\n",
      "19:26:35 Using rng type: pcg\n",
      "\n",
      "19:26:40 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:26:41 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:26:41 Read 11589 rows and found 50 numeric columns\n",
      "\n",
      "19:26:41 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:26:41 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:26:42 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a44488786\n",
      "\n",
      "19:26:42 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:26:44 Annoy recall = 100%\n",
      "\n",
      "19:26:46 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:26:48 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:26:48 Commencing optimization for 200 epochs, with 485266 positive edges\n",
      "\n",
      "19:26:48 Using rng type: pcg\n",
      "\n",
      "19:26:54 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 11589\n",
      "Number of edges: 415418\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9087\n",
      "Number of communities: 27\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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c+CnRWR5tQzdlw+AcBpW61YKnYAUj4G\nu6lasVpE2FEMwGlbFTuCHYCUV8EunbGbvhVLsAPgtGi4CiCiFQtARDwLdrtoxapAaMUCcFy7\nb0TkUKO+aWObsJgTgJfBbpp1J7RiAbgvGgY7oRsLQEQqG+zqHJ4A4L702onbCHYAhrwKdt3p\ng51ijx0A50XGhkFwsF4TdhQDEBHPgl3H2iAYnHgdr8YeOwDua/fN0kLYCrUMF3kCqDi/gp2x\nDa2DKX5ycFcswQ6Ay6K+WVoIG4NgR8UOgHfBbpo+rHClGAAvRMa0Qp1W7GjFAhDPgl3X2ml2\nnQhXigHwQrtvlhfCZhgKFTsAIuJZsOsYO812YqFiB8B9cZJ0jd2aseO6WADiX7CbshXL4QkA\nrls3NhFZCsP0vdelYgfAs2DXtVMHOyp2AByXbideXhgEO2bsAIhPwa4fxyZOppyxC4NABwGn\nYgG4K71PrBXqFjN2AIb8CXbpS23KGTsRqSnFzRMA3BWZYcVO6yCgYgdAxKtgZ6e9diJV14oZ\nOwDuivpWRFoLYRBIQ2sWFAMQn4Ld9PeJpWpaMWMHwF1pK3Yp1CLSDDUVOwDiU7AbtGKnm7ET\nkbpSzNgBcNf6sBUrIq1QM2MHQPwLdrtrxVKxA+Cs9FTs0kIoIs0wJNgBEJ+CXXeXM3Y1pTYt\n70EAroqMFZGlMA12mgXFAMSnYLfbVmxNq16cZPlEAJChwYzdgpa0FWstbzQAvgW7XbRimbED\n4LKob+paLSglIs1QJ4ls0IUAKs+fYLfbVmxdq82YlyAAV0XGpH1YEWFHMYCUP8FutwuK60ol\niRi6sQDcFPVNenJChr/TsvEEgG/BbsorxYTrYgE4Lurb5TcGOyp2APwJdrteUKyUiHAwFoCj\nImOWhm+8YcWOg7FA1fkT7DrWhkGQzhFPo07FDoCz+nG8aeOtVmyLih0AEfEq2Bk7/YCdiNSV\nEpEe+wEAOKh93XZiEWmGoTBjB8CzYDd9H1aGM3Y9DsYCcND124mFih2AoQoHu8GMHa1YAO6J\nrttOLK8fnmDGDqg6r4Ld9NdOCDN2AFwW3diKpWIHQMSnYNe1u6vYDWfsCHYA3BMZI29oxTJj\nB0DEm2CXiHStbe1uxk4LrVgAbor6VkTYYwfgBp4Euw1rk2QX107IsBXboxULwEHtN87YhUFQ\n14pgB8CTYLfbaydk2Iplxg6Ai9bf2IoVkWaoWVAMwJNgt9trJ2R4KpYZOwAuumGPnYi0wpCK\nHQBPgl36OttVK3ZwVyzBDoCDor4NhuvrUs1Qc3gCgC/Bzu66YseMHQB3RcY0Q62CYOuTZqip\n2AHwJNh19zxjR8UOgIOivrm+DysiLWbsAHgT7PbQimVBMQB3RX2z/MZg1wxDEyd0IYCK8yXY\n7b4Vy+EJAO6KjL3+SKxwXSwAEfEm2A1PxYYTf3JLjYodAGetb2vFpr/Zcn4CqDhPgt2gFbub\nGbswCHQQMGMHwDkdY02SbG0nTqVDxh3G7IBq8yrY7aoVKyI1pZhHAeCcaNt2YqFiB0BEvAl2\n3d3P2IlIXStm7AA4J70odvupWGHGDqg8T4Jdx9hApKF397dT04oZOwDOibZdOyHDIWMqdkDF\n+RPsalpdv6tzGnWlmLED4JxhK/YNPYphxY4ZO6DS/Al2u+3DCjN2ANyUVuxu2GPXoBULwJtg\n17V2V9dOpOpabVpeggAc0x7VimXGDoB4E+z2WLHTqhcnWTwPAGQnMlZEWpyKBbCNP8FuV/eJ\npZixA+Cika3YNOdRsQMqzpNgt/dWbMxLEIBj1get2De89OpahUFAsAMqzodgZ5Nk08Z7aMXW\nlUoSMXRjATglMlYHweK232YboV7nVCxQbT4Eu/Si2D20YrkuFoCL2n2ztBBuX+/UCjUVO6Di\nfAh2nT1dOyEiNaVEhIOxANwS9c0NA3apZhhyeAKoOB+CXVqx29uMnYhwMBaAWyJjWqN+laVi\nB8CHYNfZayu2PqjY0YoF4JK0Fbv98yYzdkDl+RPs9rbHTkR6HIwF4I44SbrGjmzFtkK9aWOb\n0IUAqsuLYDfrjB0VOwDOWDc2EVkKR8/YCavsgGrzItilrdi9zthxKhaAQ6JRS+xSTW4VAyrP\nh2DX3WsrNp2x61GxA+COwUWxoyt23CoGVJ0PwW44YzfiNTdeTWsR6VGxA+COyKQVu9EzdiLS\n4fwEUGFeBLu9ztgNWrFU7AC4I+pb2SHY0YoF4EWw2+seu5oKhBk7AE4ZtmJHztiFQisWqDYf\ngt0MM3ZamLED4JR0U91O606Eih1QbT4Eu46xKgjSpXS7UqMVC8A1w1OxYw5PMGMHVJcXwc7a\nhlbb78OeaHilGMEOgDMiY2WHU7Fpxa5LxQ6oMB+CXdfYPfRhhSvFADho7B47ZuyAqvMh2HX2\nHOxYUAzANe2+qWu1oEa8vZmxA+BFsLN2D9dOyPBKMQ5PAHBIZMzIPqyINLQOAip2QKX5EOy6\nxu5hO7FsHZ6gYgfAHVHfjjw5ISJBIA2tWVAMVJk3wW4vFbswCHQQMGMHwCFR34zcdZJqhZqK\nHVBlzge7XhybJGnsKdiJSE0pTsUCcEhkzMjtxKlmGDJjB1SZ88Fuz9dOpOpaMWMHwBUmTjZt\nvFMrVkRaoSbYAVXmfLDb87UTqZpWzNgBcEV75+3EqWaoWVAMVJnzwa5jrYjM0oplxg6AK9om\nvSh2bMXO2iTJ8ZkAlIn7wW62il2dGTsA7hiznTjVDMMkka6lGwtUlPPBrsuMHYDKGHNRbKrJ\njmKg2pwPdun7a++tWGbsALgjmqIVK9wqBlSY+8HOztqKZcYOgCuivhWRMXvshhU7zk8AFeV8\nsJu9FbsZ86stADdMMWNHKxaoNOeD3aytWKWSREzMETIADpjYik3vV6QVC1SWJ8Fu761YrosF\n4I60FTt+QbFQsQMqzP1gN9uMXU0pEdlkNQAAF7T7Jhimt5Gag8MTzNgBFeV8sJt9xk5EerRi\nAbggMqYZahUEO/0AFTug4pwPdh1jQxWEasfX3Hj1QcWOViwAB0R9M6YPK8MZO4IdUFk+BLs9\nl+tEpDao2PESBOCAqG/G7DoRTsUCled+sLN2zwN28vqMHRU7AA6IjB1zJFZYUAxUnvvBztjm\n2NfceJyKBeCQ9UmtWB0Eda1YUAxUlvPBrmtmqtilM3ZcFwug/LrWmiQZs5041Qw1FTugspwP\ndp3Zgl1NaxHpUbEDUHrt/oTtxKlWGDJjB1SW28EuSWQjto0ZDk8MWrFU7ACU3sTtxKlmqAl2\nQGW5Hey61ibJ3rcTi0hNBcKMHQAXpGuHpwl2LCgGKsv5YCczXDshInWlhRk7AC4YtmInvPFa\nVOyACnM72KUvr1lasTVasQAcEfWNiIzfYycizTA0ScJrDagmH4LdTBW7wYJi3oAAyi4yU83Y\ncasYUGVuB7vu7MGOBcUAHJG2YluTTsU2BzuKGbMDqmjyal9r7aOPPrqxsTHmZzqdjojEude9\nOtaKSGP2myeo2AEovfUpW7Fay3AEGUDVTA52jz322EMPPTTNdz3xxBMzP8/uDFqxM6874fAE\ngPJLZ+ymWVAs3CoGVNXkYPfAAw+cOnVqfMXu4YcfXltbu/fee+f3YFOZvRVb40oxAI6IjNVB\nsDjpV1lm7IAqmxzstNYPPvjg+J955JFH1tbWlMp7Yq8z87qTMAh0EFCxA1B+Ud8sLYTBpB9L\nr8+mYgdUk9uHJwbrTmYIdiJSU4qKHYDya/fNxAE7eb1ix+EJoIp8CHazzNiJSF0rKnYAyi8y\npjXF77FNWrFAhbkd7GafsRORmqZiB8ABaSt24o8R7IAqczvYdawNRBb1TH8XNaXYYweg5OIk\n6Vg7XSuWGTuguhwPdsbWtVbBxGHicepKcfMEgJJbNzZJZGnSdmKhYgdUm9vBrmvsjH1YYcYO\ngAumXGInInWtQhUQ7IBqcjvYdeYR7JixA1B+kTEiU1XsRKSpNVeKAdXkeLCztjHbkVgRqTNj\nB6D02oOK3VTBrhWGVOyAanI72M2tFUvFDkC5RX0rUwe7Zqg5PAFUk9vBbj6tWKXiJDFxMpdH\nAoAsDFuxU73xmqGmYgdUk8PBziZJL47n0IrlulgApRftrhXLjB1QUQ4Hu848thOLSE0pEeFg\nLIAyS1ux0+yxE5FmGG7a2CQ0IoDKIdhRsQPggF2dik1vHuvSjQWqx/lg15g92CklIhyMBVBm\n0++xk+FvvJyfACrI4WDXtVZEmjPP2NW0EpFezBsQQHlFfVPXakFN9dIeXj7BmB1QOQ4Hu/nO\n2FGxA1BmbWOm7MMKFTugwpwPdnNoxTJjB6D0or6d8kisiLTCULguFqgkl4OdtTKcEZ5FnVOx\nAEov6pspB+yEih1QYQ4Hu+68WrFaiwiXTwAos8iY5albsS1m7ICqcjjYDVqxsx+eUIEwYweg\nxEycbNp4+lbs8PAEFTugcpwPduyxA+C99m6unRCRJjN2QFU5HOwG607mMGOnhRk7ACW2q+3E\nMmzFMmMHVJDDwW5urVjNuhMApdbezXZioRULVJjbwU4FQdpInUV9sKCYYAegpKJdtmIboQ4C\nDk8AVeRwsOsaO3sfVrhSDEDpRcbKblqxgUhDa1qxQAU5HOw61s5+n5hs3TxBxQ5AWe3qothU\nK9S0YoEKcjnYGTv7tROy1YqlYgegrNJgtzx1K1ZEmmFIxQ6oIIeD3bxasTVm7ACU225Pxcqg\nYseMHVA5Dge7ebViwyDQQcCMHYDSivpWdnN4QkSaITN2QBU5HOy6c2rFikhNKWbsAJRWZEyw\ny6uxW6HuWpsk2T0UgDJyNdht2tgmyVxasSJS14oZOwCl1e6bZqhVEEz/pzTDMEkGi9wBVIer\nwW5e106kqNgBKLOob3bVh5Xh65FuLFA1rga7eV07kappxYwdgNJaN3ZXJydk2Lfl/ARQNW4H\nu7m1YpXiVCyA0mr3za52nQgVO6CqXA12823FMmMHoMzW99qKZUcxUDWuBrtBK3ZeM3aaGTsA\nJdW11iTJ0i5fd80wFIIdUD1uB7u57LETkbpixg5ASe1hiZ28PmNHsAOqxfFgN79TsczYASin\ndt+ISGuPM3YcngCqxdVgN/cZuzhJTMwqTwClk4az3R6eoGIHVJOrwW6+607qWokIY3YASijq\npxfFMmMHYDK3g90cW7EiwsFYACWUtmKZsQMwDVeDXXe+e+yo2AEoq8hY2X0rlj12QDW5GuzW\n59uKVUpEOBgLoITSVmxrlzdP6CCoa8XNE0DVuBrsutYuKBWqXVyJPUZNKxHpxfxqC6B0BjN2\nC7v+PbYVhlTsgKpxNdh1jG3NqQ8rwxk7KnYASigNdrttxYpIM9TM2AFV43Cwm9e1EzKcsWOV\nHYASiozVQbC4+8mTFsEOqB5Xg13X2nldOyHM2AEosahvlhbCPcydNEPNgmKgalwNdh1j53Uk\nVl6fsSPYASiddt/sdoldqhWGVOyAqnE42M2xFcuMHYDSiozZ7RK7VDPUJkl4swGV4mSwSxLZ\ntPOs2LHHDkBppa3YPfyJTXYUA9XjZLDrWpuIzHXGTgs3TwAonzhJOtYu7XKJXao12FHMmB1Q\nIU4Gu8FFsczYAfDdurFJspddJ0LFDqgkN4Odned9YrLViqViB6Bk9rydWAh2QCU5Gezme1Gs\ncHgCQFlFxojI3lqxzTAUrosFKsbJYJf+Ajr/PXa0YgGUTHtQsdv7jB3XxQKV4nCwm//NE1Ts\nAJRM1LcyWyuWih1QKW4Gu3nP2HF4AkA5zdKKbTFjB1SPk8GuO+9WbBgEOgiYsQNQNtEMrdh0\nxi79TRhARTgZ7ObeihWRmlLM2AEom2ErloodgKk4HOzm2IoVkbpWzNgBKJu0Fbu8x1OxzNgB\nleNksOvOe8ZOqNgBKKVZ9tjVlApVwKlYoFKcDHaDVuz8ZuxEpKYVM3YAyibqm7pWC2qP7+pW\nGFKxAyrF1WAXzDvY1ZXiVCyAsmkbs7cjsamm1szYAZXiZLDrWruodRDM8zuZsQNQQlHf7u3k\nRKoZEuyAanEy2HWMne+AnaStWCp2AEom6pu9DdilmqFeZ8YOqBJXg918d52ISF0xYwegdKLZ\nWrEtKnZAxTgZ7LpZVOyYsQNQMiZONm28PFMrNty0sYmTOT4VgDJzMth1rKU8psAAACAASURB\nVJ3jtROpulZxkvD6A1Ae7RmunUgNdhRz+QRQGW4GuyxasVwXC6BkhhfFzjRjJyKM2QHV4V6w\nM3HSj+MsWrEiwpgdgPKYvWLX5FYxoGLcC3ZpTyGLVqyIcDAWQHmk1060ZtljR7ADKsbBYJde\nO0HFDoDvImNFZJbDE2ko5PIJoDpcDXZzb8UOZ+x4/QEoi1kuik0NK3bM2AFV4V6w62YT7KjY\nASibQbCbbY+dULEDqmTv74uiDGbsOBULYDqfXbv6+VfbIvL2A/veefCmoh9nF9JTsbPtsdMy\n/H0YQBU4GOxMNocnqNgB3unH8T/9m2c+dXlt65OvPXTwp95x94Jyo1kR9a3Meio2FA5PAFXi\nxtvtelm1YqnYAd75v59/8fpUJyKfurz2e89fLOp5disyJhi2U/eGVixQNQ4GO5vJqdi61kLF\nDvDLn1xa2/7hDVGvzKK+aYRaBcGev6G1wIJioFrcC3aDdSfzbsXWVCDssQP8MrJSlW79dULU\nN7P0YUVkUWsVBLRigepwNdjN0psYqa60iPSo2AEeWWk1tn94fKmZ/5PsTWTs8gxHYkUkEGlo\nRbADqsO9YJdRKzadsaMVC/jkwyeO3dDHDAL50ImjRT3PbkV905phiV2qFYbM2AHV4V6wW892\nQTHBDvDH22/e9y/edc/tzUHdrqF1kshL3c1in2p6Ud/sW1iY8UuaoaZiB1SHe8Gua6wOgtq8\ntxWwoBjw0lfcsv/nvvJeEXlo9fBvf/2XH6zX/tcnnrvY3Sj6uSbrWmuSZGnmX2KboebmCaA6\n3At2HWPnXq6TrT12VOwA77SNEZFbFmv7aws/+va7Otb+079+2iRJ0c81QbrErjXb4QkRaYWa\nVixQHS4GOzP3ATsZtmL7BDvAO+3rLuZ61y37P3THsS++Fv2bZ88X/VwTDB575mDXDMOusaXP\nsQDmw71g17V27tdOCIcnAH+lN65uXcz1D958/K37l3/rufN/vfZaoc81Qbp8bvZWbCvUyfDY\nGQDvuRfsMmrFhkGgg4BgB/in/cZgFwbBT9x/d0Prn/ncM9dKvNPuhjy6Z00unwCqxMlgl0Ur\nVkRqSjFjB/hne0/zaHPx++85+fJG7+c+/2xxzzXBvFqx6dZPzk8AFeFYsEtEujbOohUrIjWt\nWFAM+Gdk6etbjt323qO3fury2ifOXyrouSaIjJV5VOwaVOyAKnEs2PVsHCdJFq1YEalTsQN8\n1O6PTkg/dO+dRxqLv/jkmdPtThHPNUGaR1uz3Tyx9Q2ssgMqwrFgN7goNqNWLBU7wEfXn4q9\nXivU/+Sdb46T5J8//nQJ52ujQSt2DnvshIodUBmuBTubybUTKSp2gJciYxa1DlWw/T96801L\nH7lr5Uy786tPP5/7c00QmfkcnmDGDqgU14Jdep9YNjN2dSp2gI/afbO8c93r755ceefBm37/\n7MVPv3Qlz6eaKOpbHQSLM7/umoNgR8UOqATHgl03m4tiUzVNxQ7wULtvxtS9gkD+l7ffvbwQ\n/tznn13b7OX5YONFfbO0EI4oM+5SMwyFVixQGY4Fu7QVm+G6Eyp2gHfShDTmB25ZrP3Pb3vT\n1V7/n/3NM3FprmiIjJl9O7G83ool2AGV4Fqwy7JiV1eqR8UO8E5kxlXsUl976OCDK4cfv/La\nvzvzYj5PNVF7Uh6dEq1YoFIcC3bdjGfs4iQxcVl+Xwcwu661Jk6Wp1ga8n33nDi53Pq1Z84+\nebWdw4NNNLHQOKVWqAMOTwCV4ViwSy9PzKgVW9dKRCjaAT6Z/v6GmlI/+Y67QxX89OPPFF7f\nShLpWLt9RcseqCCoa82MHVARjgW7bA9PKCUijNkBPtnVjat3LDX/4ZvveLGz8QtPns74uSaI\njEmSOew6SbVCXXhUBZAPx4LdYI9dZq1YEeFgLOCT9m6CnYh88PiRr7nt5k++8NJ/fPHlLJ9r\ngnltJ041Qyp2QFW4FuwyvXmCih3gnelbsalA5B+/7U0H67V/9YXnLqx3s3y0cdLtxHNpxYpI\nM9TM2AEV4Viw62YZ7IYzdvxeC/gj2uGi2DFuqi385Dvu3ozjf/74M6ag7SfpY8/l8IRQsQOq\nxLFg1zG2rlUYzL6zc4S0YteznIoF/LG3i7nuv/mmD584+vRr0W986Vw2zzVBe66t2FYYMmMH\nVIRrwc7aRjYDdvL6jB2vP8Afu23Fbvmeu4/fs3/5d05f+Oza1Qyea4K5t2JtkjBnAlSBY8Gu\na2xGR2KFGTvAR4PDE7tPSDoIfvz+uxta/8znvvRar5/Bo40T7TWPjpS+NtcZswMqwLFg18ky\n2LHHDvDPLMdLjzYXf/Cek69s9H7mc1/KeURjvjN23CoGVIdrwS7LViwVO8A/7b6pa7Wg9viu\ne9+x277x6K3/9eVXP37u0nwfbLzBaOD8WrEiwvkJoApcC3bZV+zYYwf4pN2ffFHseD983523\ntxq//NSZ0+31eT3VRHPfYydU7IBqmM+vg/mIk6Rn4wyDndIi0qNiB3gkmjnYNbT+ifvv/oE/\n+9xP/dUX7z+478pm/2hz8aHVw3csNef1kNtFsxUab9AMQ2HGDqgGl4Jd18ZJZveJiUiNGTvA\nO21jbm82ZvySN9+09M6DN/3lK1cvXthIP/n4uUv/+G1vet+x22Z+wNEiM5+LYlPM2AHV4VIr\ndnDtBDN2AKYW9e3sN65e7m5+du216z+xSfILT57esFlFpXbfzOvkhNCKBarEwWCX9YwdwQ7w\nxaaN+3E8e0J6/MprdtsVFB1jv3g1mvGbd7JuTGt+77rWoBVLsAP851Kw61orWbZi64rDE4BX\nBtuJZ35p9OPR206ym9yI+naOFTtasUB1uBTs0kussz4V2yfYAb4YbCeuLcz4PXfftLT9Qx0E\nd+0b8fnsTJxsWLuPViyA3XMr2FkRaWY3Y0crFvDL3i6K3e6ufa1vOHLLDR9+xx1HD9RnjYwj\n7fkatJ0Mgl1mE4EAysOpU7Em21ZsGAQ6CAh2gDeG94nN4aXxY/ff/Zb9y//hwuXn252aUv/o\nvju/OcMjsfPpIG+pKRWqoMO6E6ACnKrY2WwPT4hITSlm7ABvzLH0FQbBd9xx9Ne+9p1fcesB\nCeSbj94WzP6lO5jvRbGpVhhyeAKoAqeCXcatWBGpacWCYsAbaUKavRV7vZVWY9PGlzc25/id\nN0jzaGt+e+xEpBlqZuyAKpj84rDWPvrooxsbG2N+ptPpiEicca0r61asiNSVYkEx4I12BsFu\ntdUQkXNR53CjPsevvV5krMz7sVsEO6AaJr84HnvssYceemia73riiSdmfp5xst5jJyI1rZix\nA7wx91MIIrKy1BCR8+vdr7z1wBy/9nrrc70oNtUM9dpmb45fCKCcJr/vHnjggVOnTo2v2D38\n8MNra2v33nvv/B5shE7Ge+xEpM6MHeCRLFqxg4rdeneO33mD4fq9Oc/YUbEDqmDyi0Nr/eCD\nD47/mUceeWRtbU3N6b7qnXSNDQJZVFkGO63SVyoAD7T7pqZUba6vppvrteWF8HyUYbBbz6AV\n2wz1po1NnIQqu1MfAIrn2OGJhtZBli8lTsUCPmkbM994lLq91cijYjfvYCessgMqwKlgZ20j\nyyOxwowd4Jeob+Ybj1KrrcaVzV521f3ImGB4D9i8pN+2zio7wHcuBbuusZkO2AmnYgG/tPs2\ni4rdSmtwfmLu35yK+qYRajXX9gS3igEV4VKw6+QQ7LSKk8Qkoy/8BuCWqJ9JK3Z1Kdtg187g\nsZthKAQ7oAIcC3aZ7joRkbpWIsKOYsADmzbuxXEWrdiVVlOyDHbrxs73SKwMK3ZcPgF4z6lg\nZ22m106ISHp6jjE7wAPpjatzuSj2Bsdai2EQnMvsYGwWo4GtQSuWGTvAc84Eu34cmzjJuhU7\nCHaM2QHuy+LaiVQYBEeai+fXO3P/5lSUwWFeKnZARTgT7DrZ3ycmw1YsFTvAA1ksDdmyutR4\nYX0ji3ncrrUmTpbm/a5rMWMHVIM7wS77aydka8Yu5t0HOC+Laye2rLaaJkkudsZdybM3Ud+K\nSCubih3BDvCeM8Gum0vFLm3F9iynYgHnZdeKleHGkyzG7KJsCo0tgh1QDc4Eu/R9lPWC4kEr\nlood4L5MW7GDYJfBmF165mPurdgmC4qBanAs2OV0eIIZO8B9mbZij2e2yi6jx17UWgUBFTvA\ne84Eu26uM3YEO8B5mbZilxbC/bWF7ILd3AuNgUgz1JyKBbznTLDLpxVLxQ7wRttYEZn7pt8t\nq0uNsxnM2GX32M1QU7EDvOdYsMtp3QkVO8B9Ud8sKJX+Q52F1VYz6purvf58vzajip2ItELN\ngmLAe84Eu5xasUoLV4oBXsjixtXrZXQwdhjs5v+uoxULVIEzwW7Qis368IQOhBk7wAvtvlnO\nIB5tWV3K5GDs8FRsFhW7kFYs4D3Hgl32d8VqYcYO8EIWN65eb7WVycHYqG91EGTxS2wz1F1j\nM7gsA0CJOBPs8llQzJVigDfafbOc2ckJETncWKwplUUrdmkhDOb7pSIi0gp1MrzFB4CvnAl2\nHWvDIFhQ2T5wXbHuBPBBL457cZzpjF0QyO2txXNzr9gZM/ftxKl0qwA7igG/uRPsjM16wE7Y\nYwf4ItNrJ7astJqXuhvzfWNk10HmVjGgClwKdln3YUWkRisW8EKm105sWV1qJIlcWN+Y43e2\nMwt2DYIdUAHOBLtuLsEuDAIVBAQ7wHWZXjuxZWVwfmJuB2OTRDrWZrRUuRWGIsLGE8BvzgS7\njrVZXzuRqinFgmLAdfm0YlfnvcouMiZJMlliJ8PDZ+woBvzmTrDLpWInInWtWFAMuC63Vmww\n140n6RK7jB47nbGjYgf4zY1gl4hs2DinYKcUhycA16U3rmYd7Ba1vnWxPseDsWkebWXTim0y\nYwdUgBvBbtPaOEmy3k6cqmnFjB3gukErNss9dqmVpca5qDuvpb9RP8M82mTGDqgAN4JdPveJ\nperM2AHuy6cVKyIrrcaGtS9vbM7l2wb3iWUzY5e2YrsEO8BrLgU7ZuwATCmfU7GydbHYnM5P\nZFpobIYsKAb850aw69r8gh2nYgEPtPsmVEG6cjxTK0vzvDE2yvIwbyvUATN2gO/cCHaDViwz\ndgCmE/VNDuU6EVltNUVkXucn0hm7jIKdCoK61szYAX5zKdhxKhbAlNp9s5z9yQkRuWWx1gz1\nvFqxgxm7zN51rVBTsQP8RrC7UV2rOElMMq9TbgAKkN3FXDcIRFZajXNzunxiPeO9yk2CHeA7\nR4KdzfFUrFYiwvkJwGmRyakVKyKrrcYrG725BKZ239S1qqms3szNUHPzBOA3N4Jdej4/pz12\nSokIY3aAu/pxvGnj3ILdylIjmdP5ichkdVFsqhWGzNgBfnMj2OXZih0EO8bsAGflc1HslpX5\nnZ+I+iajJXYpWrGA91wKdnm2YqnYAe5qZ3l/w3ZzXGWX9WhgK9Q2SXi/AR5zI9ile+wyuj/x\nBoMZOyp2gLNyu3YidXtrUQXB+Xmcn4iMybQVy45iwHtuBLu0YreY/a5RGbZiOTwBuKttcm3F\nLih1uFGfvRVr4mTTxllX7IQdxYDXnAl2da10EOTw16qlrdiYFx/gqsF9YrnU+FOrrcaF9Y14\ntjVJ6RK7TAuNjUHFjvcb4C1ngl0+JydEpM6pWMBxObdiRWR1qdmP40vdzVm+ZHhRbIbvunSg\nhYod4DE3gl3X2nx2nQgzdoD7cj4VKyIrrYbMfDA204tiU8zYAd5zI9jlWbFjjx3guiIqdg0R\nORfNdH4iMpmfEmPGDvCeM8Eun10nQsUOcN8w2OX00pBhxW7GHcU5FBqbBDvAd24Eu1xbsUoL\nFTvAZW1jwiBYzOulISL7awv7FsJzs62yW88+jzbDUDg8AXjNgWAXJ8mmjfNrxepAqNgBLst6\nze9IK0uNGSt26anYjK8Uo2IHeM6BYJfntRMiUqNiBzgu6ps8B+xSq63m1V7/Wn/v5xKivhVa\nsQBm40Kws/ldFCtcKQa4r11EsFuZ+WKxdvZnPoYVO07FAt5yINh1jRWRHGfsODwBuK3dt/m3\nYgcHY2e4WCwyJhhmr4wsKLWgFDN2gMccCHY5t2I5FQs4zcTJhrVFtGJnPRgb9U0j1CrjK3Za\noaYVC3jMhWCXbyu2RisWcFkOF3ONdKS5uKDULAdj8znz0Qw1FTvAYw4Eu0ErNq9gFwaBCgKC\nHeCoHC7mGkkHwdHm4iyXT6wbm8P9tq1QM2MHeMyBYDdoxea4kqqm1CatWMBN+d8ntmW11bjY\n2ejv9e0R5VSxC6nYAR5zJtjlVrETkbpWzNgBjsr/PrEtK0sNmyQvdjb29qdHxixlf1tGkxk7\nwGsOBLtuvjN2IlJXqkcrFnBTDktDdpJuPNlbN7ZrrYmTfFqxvTg2cZL1XwhAIRwIdgW0YrVi\nxg5wVLGtWBHZ2/mJdDtxK5fDEyKyzpgd4Clngl3OFTtm7ABHFXUqVkSOLzVlrxtPorzyaCsM\nhcsnAH85EOxyPhUrIjVNKxZw1aAVm31Pc7tmqA/Wa3sMdianw7yNQcWOYAf4yYFg17E2CKSe\nYyuWih3grgJbsSKystQ4G+3l8okcK3ZcFwv4zIVgZ2xD62x3sb8RFTvAXVHf6iDI7a6aG6y2\nGh1j1zZ7u/0TczvM22DGDvCaG8Euzz6sULEDXJbe35Dnr4LXSw/Gnt/9+Ym2sSKylMupWKFi\nB/jLgWDXtbaZYx9WROpaxUliEtYBAO5p900hJydSq0tN2dPGk2ErNo89dkKwA/zlQLBbNzbn\nrkpdKxGhGwu4KOqb/O8T25JuPNnD+YmcT8VyeALwlQPBrpt7K3ZBKRFhlR3gomIrdrct1uta\nndv9+Ynhqdic9thRsQN85Uawa+W7uaCeBjvG7ADXmCTZsLbAYBcEstJq7KkVm9OZj+GMHYcn\nAD+VPdj14tgkSSGtWCp2gHOivkmK23WSWm01Xupu7vYFEvVNK8zj+D8VO8BvZQ92g2sncj88\nISI9KnaAa3JbGjLGSquR7H7MLjI5dZAXtVZBwIwd4KuyB7v8r50QkZri8ATgpHYZgt2eDsZG\nfZNPoTEQaYaaih3gq7IHu461MtyomZta2oqNefEBjin22onU4GDsLs9PtPsmh5MTqWaoWVAM\n+Kr0wa6Iil2dU7GAm9KzpYW3YoNgd63YJJGOtbnl0RYVO8BfZQ92XWbsAExt0IrN9xz9Depa\nHVqs76oVGxmTJHlsJ041Q82MHeCrsge79NfKvFuxzNgBbmr3rRTdihWRlVbj/Hp3+strclti\nl2qFYdcS7AA/lT7Y2SJasZo9doCTynAqVkRWl5qbNr68sTnlz+d27USqGequsTG3JgI+Knuw\nK6QVW2PGDnBTGU7FishKqyEi098/EQ0KjTm96FqhTkS6vOIAH5U92BXSimXGDnBUu29ULvc3\njLfbG2NzPvOR9kA4GAt4yY1gl/seOy1U7AAHRX2zlMv9DeOtLO0u2A22tOS27kRz+QTgrdIH\nu+Jm7KjYAc5p93O6v2G8g/Xa0kI4/cHY3GfsQiHYAZ4qe7ArZt0JM3aAm9omp/sbJlppNc5H\n0we7XA/zDluxBDvAQ2UPdh1jQxWEKtfWChU7wFFROSp2IrLaaqxt9tJS3ETDdSf5HZ4QkQ4z\ndoCPSh/srM25XCecigXcZJOka2xJgt3Kbs5PrOe+7kSo2AGeKnuw6xqb84CdiIQqUEFAsAPc\nEvVNUoLtxKnVpYaITDlm1+6bmlLpr5Q5aDFjB/ir7MGuU0SwE5GaUiwoBtxSkiV2qV1V7KJ8\nC41U7ACPORDsCllJVdeKGTvALZGxUppgd6zVCIPg3HTnJ6K+yW07sQxn7LoEO8BHZQ923SJm\n7ESkrhR3xQJuyXkb3HhhEBxuLp5fn+ryiXbftHJ8bBYUAx4rdbBL0mBXSCtWK2bsALeUqhUr\nIqutxgvrG2aKK1kjk+th3maoA2bsAE+VOthtWJsked8nlqozYwe4JipbsFtqmCS51NkY/2Mm\nTjZtnOeZDxUEi1ozYwd4qdTBrlPEduJUTdOKBRzTzndpyETp+YmJB2OHS+xyfexmqKnYAV4q\ndbDrFnFRbKquODwBOKZ8rdimiEw8PzF87FxfdK1Qs6AY8FKpg136C2UhrVhm7ADnlLAVK1Ns\nPEkfu5XvYzdDWrGAnxwIdkVV7JixA9zS7hsVBIUMb4y0vBDury2cm3QwNt3SknMrthWGtGIB\nL5U32EV985evXBWRV7qbkw+VzVtdqzhJpjnOBqAk2sa0Qh3kerP0BCutxpSt2JxHA9OKHS84\nwD8lDXZ/evnKR/74s797+oKI/PbpF37gzz738kYvzwdI7/bh/ATgkKif69KQaawuNdp9c7XX\nH/Mz6wXN2MVJsmkp2gG+KWOwe2lj8589/vT1r8Inr7b/5ee+lOcz1LQSEcbsAIe0yxfsprlY\nrKhTscKtYoCPyhjsPnX5yvZE9ddrV69s5le0qyslIozZAQ5p9015dp2kVpcmH4yN+lYKaMWG\nwo5iwEdlDHZXRwW4RGR8O2O+6lTsAKfESdK1tiT3iW1ZHayyG3d+YnAqNt9TYq0FLQQ7wEdl\nDHaHGovbP9RBcNtiPbdnSINdL+atB7hh3dgkKdGuk9ThRr2m1ISKnTFB/hU7zXWxgJ/KGOze\nc/jggfrCDR++9+iteb74hocnODQGuOFayZbYpVQQ3N5aHD9j1+6bRqhzPs2bzthRsQP8U8Zg\nt7QQ/uxX3Pvmm5bSPwwCef/KoR+692SezzA4PEHFDnBE2bYTb1lpNS92N8bcZBMVMRrI4QnA\nV6V7CabuXG798lff/2J3Y22jt7rU2F+7sYCXtcHhCWbsAEeU7T6xLSutRpLIhfWNk8vNkT8Q\nGbuc+2ggFTvAV2Ws2KWCQI41F99+8778U528PmNHsAPcUMia32msDC4W2/H8REEVu1Co2AE+\nKm+wKxYLigG3REWs+Z3G6qRVdpExS7k/dnoIt0uwA7xDsBttsO6Eih3giNK2YleXGoHI+R0O\nxm5Ya+KkqFYsp2IB/xDsRqsxYwc4pbSt2IbWtyzWzu1QsWv3rYi0cn/sFjN2gKcIdqMxYwe4\nJb2YK//S1zRWW81zUXfk8qSooDy6oNSCUszYAf4h2I1WU1qo2AHuiPomCKRVymC3stToWvvK\nxog7dYYXxRYwGtgKNRU7wD8Eu9Go2AFuafftUhjmu+V3WmMuFiuqYicizVB3mLEDvEOwG409\ndoBb2kUsDZnSYOPJqPMTBe5VboWaVizgH4LdaDUqdoBTor4p4ZHY1GqrKSIjz09ExorIUhEd\n5GYY0ooF/EOwG42KHeCWtjHlPDkhIrcs1hpaj6zYDQ/zFjBj16RiB/iIYDdaqAIVBAQ7wAlJ\nIuumvK3YQGRlqTFyxi7dJFfIk7dC3Y9jE488rQvAVQS7HdWUohULOCEyJknKuJ14y2qr8cpG\nr2tvrJANKnbFtGLZUQx4iGC3o7pW3DwBOKHAIwhTWmk1klHnJ6K+VUHQKGbdSSjsKAa8Q7Db\nUV0p7ooFnFDgpNqUVpfSjSfbgp0xS6EuZElLY1CxI9gBXinvL7iFq2nFjB3ghHZxk2pTWmk1\nReT89mBX3GFebhVDyV3Z7J2JOstheHK5FapS7qgspfK+BwtXU7RiATcMWrFlPRUrIiutRRUE\n50a0Ys1NtYVCHokZO5SWiZNf/uKZU+cuxUkiIkcaiz9y351ffsv+op/LDbRid1TXtGIBN7RL\nP2O3oNThRv38toOxkbGFnJwQKnYosV95+vmPnb2YpjoRudjd+Im/eurFzkaxT+UKgt2OODwB\nuCLqWyl3K1ZEVluNC+sbW/+ukuGWllZBo4EcnkA5xUny6IXLN3zYi+NPvvBSIc/jHILdjjg8\nAbiiXdyNq9NbWWr04vhSd3Prk/VCt7Q0OTyBUoqMHfn7xuXr/tnBGAS7HTFjB7ii/OtOZNTF\nYul9Yq2CW7HM2KFcWqGu6xHh5FJ3gwLzNCa/UKy1jz766MbGuN52p9MRkdivGFTXKk4SkyRh\nwGEcoNSu9U0gslTENrjprbQaInI+6n7VrQfST4odDWwwY4dS0kHw3iO3bu/GPn7l2t/5o898\n+/EjHzpxrFnuf9iLNfmF8thjjz300EPTfNcTTzwx8/OUSE0pEenZOOR/QEC5RX3TDLUq9+9g\n6Sq76zeeRIWu3+PwBErre9964tlr689ci9I/XF4If/CtJ0Md/B9Pn/2NZ89/7NylD584+t8e\nPzqysIfJwe6BBx44derU+Irdww8/vLa2du+9987vwYpX00pENm3MbwZAybVNYdvgpre/trBv\nIbz+xtjIFFmxW9RaBwEzdiihZqi1CmpKfe9bT9yyWHvbgX3pPybvPnTwP7/48m88e/5Xnz77\nf5158UPEu1Emv1C01g8++OD4n3nkkUfW1taU8uq/3LpSIsKYHVB+7eLW/O7KylLj+lV2UXEX\nxaaaoaZihxL60rX1p662379y6KHVw9d/HgbB+47d9g1Hbv3DFy7/5rPnf/Xpsx87e/HDJ449\nuHq45lcCmQX/Rewo/SWgR7ADSi/qm5IfiU2ttppXe/1r/cF5hcIP8zZDzYJilNDvP/+iiNyQ\n6raEKvi2lcO/8553/ch9d8aJ/NJTZz7yx5/9vedf7PPvaxEh2I1RH7Zii34QAOMkiawb60bF\nbnh+Iv3DtA1aYLBrUbFD+bT75rFLr9x3YN9d+5bG/Fga737rPV/+fW89YeIkjXefOH/JXrcq\nspoIdjvaOjxR9IMAGGfdmDhJnAh26fmJrTG7QcWuuCneZhgyY4eyefTC5U0bf/D46HLdDepa\n/a07jv7We77s+956YtPGP/+F5z7yXz77ifOX4grHOwdehUUZHJ6IeesBpdY2BU+qTW9QsRse\njF0vQSuWih1KJUnk1LlL+2sLX3fo4PR/1qLWf+uOo9+6cuhjZy/+LEnBPgAAIABJREFUn6df\n+PkvPPd7z7/4XSdv/8ajt8aJ/D9nX/yzl15t9/t37lv6u3fevtpqZPf8ZeDAq7Aog8MTVOyA\ncnNiO3HqaHMxVMHW+YnI2JpSBQ59t0K9YW2cJCXfFIPq+POXr1zsbPz3b1pZ2P0/Fw2tv+vk\n7R9YPfIH5y7+znMXfuZzX/rd0y/EEp+PBms9Trc7j118+V++6953Hrxp3g9eIrRidzQ8PFHd\nci7ghGLX/O6KDoJjzcWtil3hh3mboU5YZYcy+YNzl3QQfNvKVH3YkZqh/q6Tt//217/ru9+0\ncrG7sZXqUiZOPvrFMzM/ZqkR7HZUG1TseOUBpRb1rYi0XAh2IrLSar7Y2TBxIiLrxrQKXZOZ\n3mZGsENJvNDZ+MtXXn33oZtvXazN+FX7FsLvvmv1PYdH9HOfvbbe9frf7AS7HQ1OxXJ8Gig3\nhyp2IrK61LBJ8kKnKyXY0pJuX+f8BEri1LmLSSIfXD0yry+s5nK7Kv49T4lTsYAT0mC3z5Fg\nd/35iWslaMUKFTuUw6aN//DCS8eXGvfPbwDubQf2bf/wTftaDe3zhVIEux2xoBhwQuFrfncl\nPZF3LuqaONm0cTkqduwoRvH+44svt/vmg8ePzPEgzzccvfW+N2a7IAi+960n5vdXKCM3XoWF\nqCktVOyA0nPoVKyIrC41ReT8ejcqwZaWFhU7lMbHz19qhvqbjt42x+8Mg+Dnv/K+rXUn13pm\nbbOnfT8D7sarsBDM2AFOaBsTDDNK+bVCfXO9dm69O8yjRT52MwyFGTuUwBdevfbMa9EHjx9p\nzvsf5FAFHz5x7MMnjonIC52Nv/8nf/2LT575377mfo/THa3YHbHHDnBCu28aoXbot/DVVuNc\n1Ek7yMUe5qVih5L42NlLIvLgDFtOpnGsufjtx4986Vr0/77wUqZ/oWIR7HZUY8YOcEFU9BGE\n3VpZaqwbe269K0W3YoeHJ5ixQ5Gu9vp/cnntyw7uP7HczPqv9ZE3rRys137l6ec9LlQT7HZE\nxQ5wQuFrfncrPT/x5NW2FH3mo6m1iHS83umF8vv4uUv9eNrLYWfUDPX33H38aq//W8+ez+Ev\nVwiC3Y5CFaggINgBJdcuehvcbqUbT54aBLtiZ+xoxaJgNkk+cf7SbYv1r77t5nz+it987La3\n3LT0e2df3LoDxjMEu3HqSnF4Aiiz9EasYhuau5UejD3T7kjRrdhWGAbDqzuAQnz6pSsvb/S+\nbfVQbmOyQSDff89JGycffcrPu8UIduPUtGLGDhURJ8ml7uaGa125jrE2SVzZTpw6tFiva2WT\nRIre0hIEsqi139croeR+/+zFUAXvvz2PPuyWe/Yvf+PRW//85Vf/68uv5vnXzYdLb8P81ZVi\njx28Z5Lkt589/2/PvLhhbSDy1bfd/AP3nDzUqBf9XFNxaztxKghkpdV49tq6lODJm6FmQTGK\ncjbqPr722nuP3nqgvpDzX/p/fMsdf/rSlV988vSXfd07F/y6ecyrv5m5q2nFjB2899GnzvzG\ns+fTWl0i8umXrvzIX3zBlf/lu3VR7JZ0zK4M6/daoWbGDkX52NmLicgHj8/tctjp3Vyv/Z2T\nt7/Q2Ug3rfiEYDcOM3bwXsfYj5+/8b12sbPxJ5fXCnme3VofVOzc2E6cenmj9+L6hoioIPg3\nw0hdlNZC6PHeB5RZx9j/78WX7trXumf/ciEP8KETR482F3/z2XOvbvYLeYCMEOzGqWtasfDc\npe6GiZPtn5+L3Dgv1jaOVexOtzt/708++/S1SERskvzms+f/pz99vMCaWZOKHQryyRde6hj7\ngSLKdakFpR5+y4l1Y3/tmbNFPUMWCHbj1BSHJ+C5nU5luhKVrvXSYJf3gM6e/dozZ28IUufW\nu79/9mJRz9PUumPsiGgPZOzj5y8tLYTvPXJrgc/w7kM3f8Ut+//DC5e/+FpU4GPMF8FuHGbs\n4L3bGvU337R0w4dhEHzNoZx2Ss0oHfxfKnpSbXqff/XalB/moxXqOEmcOw0N1/3N2mtn2p1v\nvf1Qei17gb73rSeUBL/81Blvfr0h2I3DjB2q4MfefteR5uLWH9a1+kf33nnsuk/KzNHDE+XB\njmIU4mPnLgaBPLSa65aTkY4vNT+wevgLr177Ty++XPSzzAfBbpyaVnGSmMSbHA+McHyp+etf\n984DtQUR0UHw61/7Ze9fOVT0Q03rmmvB7m0H9k35YT6aYSgEO+RrbbP36ctXvvKWA0fL8Qvk\nd9+1ur+28CtPP+/HTkeC3TjpdbGcn4D34iS52u+LiE2SBZ3T/ve5iPpGRFruBLvvuft4842N\n49VW49uLmx+nYof8/cHZiyZJCtlyMtLyQvj37l59ZaP3u89dKPpZ5oBgN07a+2fMDt470+4k\nieyvLYjIpc5m0Y+zC1HfNLQO87qMaHYnl5u//nVf9uDK4bv2te49sPyRN6189N33N4ubEWwt\naBGJ2FGMvJg4+fcXLh9pLn7lLQeKfpbXvf/2Q2/a1/p3Z1682N0o+llmRbAbp5ZW7Bizg++e\na6+LyFfdekBELm+49F671jfLNWfKdalbF2s/fN+d//u73/Gvv+rt/8Ndqw1d5MmPptYi0qVi\nh7z80aVXXt3sf2D1cKl+HVNB8IP3nOzH8Uefer7oZ5kVwW6cQcWOYAffnW53ROSrD90sDlbs\nlnfY2IJppMVCdhQjNx87e7Gu1bfcXrpB3vsO7HvPkVs+dXntM69cLfpZZkKwGyet2NGKhfdO\nt9eXFsJ0hP9y16Vg1+4bh05OlFCLwxPI0bPX1p+82n7vkVv3lfIf24ffcmJR61966ozThyYJ\nduOkFTsOT8B7Z9qdk8vN/bWFRa0vuTNikoisG7tUyn9DuIKKHfL0sbMXRUqx5WSkWxdrf/vk\nsbNR5xPnHL5AlmA3Tm3QiuWVB5+91N1s983J5ZaIHGrUL7lTsesaa5OEit0sWiEzdshJ1Df/\n6eLL9+xfvnvbUvTy+M4Txw436r/+pXOv9Vy9QJZgN06dViwq4HTUEZGTy00ROdyoX+5uutKE\nYDvx7IYVO07FInOPXri8aePybDkZqa7VP3zzHe2++fUvnSv6WfaIYDfOoBU76op0wBunr62L\nyFbFrhfHr272in6oqaTBjlbsLJixQz4SkU+cv7y/tvDfHD5Y9LNM8PVHbnnHzTd94vzldF2A\ncwh24wzWnXixihrYyel2JxC5Y6kpIocadRFxpRsbDSp2zlwUW0KhCmpKMWOHrP3Fy69eWO9+\n28qh9F+sJfeD954MRH7hidMu1nUc+O+3QDXWnaACTrfXjzQX05bc4caiuHMwtm1oxc5BM9RU\n7JC1j529qILg/SslPTZxgzuWmt+6cujzr17740trRT/LrhHsxmHGDt7rx/GF9W7ahxWRw4OK\nnRsHYwetWPbYzaYV6g4zdsjS5e7mX75y9d233Zz2BJzwD+4+vm8h/OhTZ5zLAAS7cYYzdo79\nPxWY3vNR1yTJncvN9A/dqthFHJ6Yh2YY0opFpn7/7MW4TJfDTmN5IfzIXasvbWz+2zMvFP0s\nu0OwG6emtLDHDl473V4XkRPDit3++kJdK1dm7DgVOxctWrHI0qaN//DC5eNLjXccvKnoZ9md\nD6wePrnc/N3TF1z5XTdFsBuHK8XgvTPt13ediEggcmixftmtVizBbjbNUFOxQ3b+88WXr/XN\nB1aPlOlu2KnoIPi+t57ctPGvPP180c+yCwS7cWoqEGbs4LXT7fVFrY81G1ufHG4uXnJklV3E\njN08tELdj+M+v8EiG6fOXWpo/U3Hbiv6QfbinQdv+tpDBx+7+MrjV14r+lmmRbAbp661MGMH\nr51ud+5YagTX/Sp9aLG+aWMntq5Hxja0DpVzhYByaYahcKsYsvHk1fbTr0XvO3ZbeseJix5+\nyx01pX7pqTOxIxfIEuzG4VQs/Ha117+y2btzX+v6Dx1aZdfuGwbsZpduumHMDllIL4d9sKyX\nw07jSHPxQyeOPntt/dELl4t+lqkQ7MYJVaCCgIodfPXcG09OpA436yLixJhdu28YsJvd8FYx\ngh3m7Gqv/8eX1t5x8KatKV5H/Xd3rtzWqP/q02ev9R1YDMQ7cYKaUlTs4KvT195wciKVbjxx\nomIX9c3xJbf/hVEGrUHFzoF/Y8Etnzh/uRfHH1x1acvJSHWtvufu4//i8Wd+6M8/3wz1zfXa\n1x+55YEjt5RzCoRgN0FdK07Fwldn2q/fErslbcU6cbw/ohU7D7RikYU4Sf79+UsH67WvOXRz\n0c8yB4taicjzUSf9w09dXvuLl1/9sbffVehDjUYrdoK6Uuyxg69OR51bF2v73piNbq7Xakpd\n6pS9Fdu11iTJEhfFzoxWLLLw6ZeuXO5uPrR6OAzKWdjahThJfuGJ0zd8+MkXXvr8q9cKeZ7x\nCHYT1DStWPgpTpKzUeeGATsRCURua9TL34rlPrF5aYWhULHDvP3B2UuhCr515VDRDzIHFzob\na5u97Z+XcwcK78QJ6opWLPx0obOxaeORQ82HG/UnXm3n/0i7ErGdeE6GFTtm7DCrDWt/57kX\n/ujSK69s9jaMfduB5YP1WtEPNQfJDotObCn3n1Cxm6CmFadi4aXnro0YsEsdatS71pZ8lV1a\nsdtHsJtZeniiS8UOs4mT5Ef/8snfeu78hfXuhrEi/397dx7fxl3nDfw3M5JGI0uyZMmWfMdX\nLts5nKQlpSWU0lK2B7uwXM/z8LAsD8u5dCmFcrWlBfZpabflpoWFZ9ld2LLPU650C7S0pUBo\naRI7iRPHiW35tmVbsiWNbo1mnj/GNmmsRJJ1zEj6vF/8QSa29OskGX/1+30PMrjCPzExp/S6\n8qCxikv5qOm2mIq/mLQQ2KWBHDsoVykrJ2SOUiiMxTyxfEGOHeTFS0srG3PO/mVkqgyGmmgo\n6v3bt1x08cpa6z67RYnlpIFnYhrIsYNy5eLDGppqqeI2/lb9Wo/ibdXGoq8rU0EhSRDY5UMV\nJk9APpz3BzdeDAnJ2XB0S+m3JXpjk6PeoH/cNTsRDNfotNc11L6pVaXTb/FMTAM5dlCuxvlw\nS5Uh5TwuuZWdynsUyzl2JhRP5IxlaIaiUDwBOdLQqc8AtZe4XnL21FTvqalWehXplcntLhwd\nQ4uSJJTIhDiADIWFpDsS7bhEO/iSaGUXiAuEEJMOgV0eGDQMAjvI0T5biqCn3qBvMOiLv5hK\nhsAuDXlcLNLsoMyM8SFpwzCxdTZWp6VplefYBQXs2OWNQcOgKhZytMNienPrK4ZMsAx9e0+H\nOs8ryxieiWnoGJoQEkuKcn4xQHlwrVZOpN6xoyhSp9ep/Ch2rXgC/zDzoAo7dpAPH9nZHk6K\nv5pZ2Gkx9VjNb26tr+NYpRdVcRDYpbG6Y4c0OygvLj5MCOkwp96xI4Q4Of1Zv6pb2fEJgWXo\nsknfUZZBo5lX/awRKAkLkSjL0I9c2YN/m0rBfU+DlXfsENhBeXHxIbNWc5neoQ6ODQtJeVdM\nnTAoNo8MGgZVsZC7uCgO+fhdVjOiOgXh1qeho1ePYpVeCEDeSISM8+HLbNeRtfoJt4pPY3kE\ndvlTpWFiyWQSVWKQm9MrgVhS7FNld7fKgcAuDXnHDsUTUE7ckWhYSKZsTbzOqfrCWD4hYFBs\nvhg0jIRxsZCzfq+fENKXqjwWigaBXRqrxRMinndQPlyBMCGk7RKVEzKnQe3DJ4ICduzyRu5R\njMAOctTv8Zm1mk6TehubVwIEdmmsFU/ghALKh1wS23HZHTuVt7KLJpOCKCGwyxcOU8UgZyEh\neT4Q2murptDgRFEI7NJgGeTYQblx8WGaoi4/5MfO6jQUpdocOz6BeWL5VKVhCCFhtLKDHJzw\n+kVJ6rMhwU5hCOzS0K02KMYHWSgfLj7UaNDLH1ouhaaoWo51h1W6YyeX62LHLl8Mq4EdHnSw\nef1eHyFkLxLslIbALg0d2p1AeYklxdlw9PKVEzInx6p2xy6IwC6vqhDYQc76vX67XtdUxSm9\nkEqHwC4NFu1OoLxMBMOiJF1q5sSFHBwbEpJBVbayWxs7gcAuPwwaDUGOHeRgJZaYCob34xxW\nBRDYpbHa7gQ7dlAu1oaJpd+xc3DqLYzlBezY5RN27CBHx70+iZC96GCnAgjs0tDRDEEfOygj\n8jCxTHbs1NzKbvUoFn3s8sSA4gnIzWqCXQ0S7JSHwC4NjBSDMuPiQwYNI7epuzyniodP4Cg2\nv1YDO1SJwWad8PpbjZxdf8kphVA0COzS0NEUQY4dlJFxPtxmMmTSZ8rJ6Ylad+zWqmIZpRdS\nJtCgGHIxE4q4I7G9SLBTBwR2abAMQ5BjB+XCG4v74olMEuwIIXa9jqEodebYBbFjl1cGhqFQ\nPAGbNYBJYmqCwC4NVMVCOck8wY4QwlBUrV63oNajWJah5TaTkDuKIpyGwY4dbE6/10dT1G4k\n2KkDHotpaGiKpijs2EF5yLwkVubk9OrcseMTghGVE3ll0DAhFE9A9iSJnFgObDVXoUpdJRDY\npaejaezYQXlw8WGKkLbLDhO7kINj+YSgwhO6YELAT5H8qsKOHWzKKB/yxxN9aHSiGgjs0mMZ\nGlWxUB5cgVAdx2aemrbW8UR1p7G8gMAuzwwajQojeFC/Aa+PIMFOTRDYpcfSNPrYQRlIStJU\nKJL5OSxZ61GswsLYYCKJyon8MmDHDjal3+vX0XS3xaz0QmAVArv0dAyOYqEcTAUjCVHsyKxy\nQrbWyk5dgV0sKSZEETt2+VWlYUKCICm9DCgtgigNLge6rSa55yuoAf4k0tPRNIonoAzIlRNt\nWe3YGdQ4fGKtiR0Cu3wyaBhJIlH0KIZsnPXzkWSyDx3s1ASBXXrIsYPysNbrJIvArlbP0hSl\ntuETGDtRCOhRDJvQ70GCneogsEsPOXZQHlx8SEvTTVXph4mt01CUXa9T21FsUMCOXf7JU8VQ\nPwFZ6ff6DRpma7VR6YXAnyGwSw85dlAeXHxoi9HAUJmME/szJ8eq9ChWg3li+bQ6LhaBHWQs\nmkye9fN7aqqzfapAQSGwS09H4ygWSh6fEJai8awqJ2QOTu+PJ1T18x5HsYWwtmOHHsWQqVPL\nAUGU0MFObRDYpccytChJgoRyMShhm6ickK21slPRpl0QxRMFUIUdO8hSP0bEqhICu/TkcbFI\ns4OStlo5Yd7Ejh1LCFmIqiiwQ1VsIRg0GoIcO8jGcY/PympbM55kA8WBwC49HUMTQtDxBEqa\nvGPXsdkdO3dYRYWxOIrNuxfc3q8NuQghXzkz9sUT55eicaVXBGoXSAiuYKjPZkF6ndogsEtP\n3rFD/QSUNBcftui0Fp022290qm/4BI5i8+upmYV7B4bl2D2eFJ+bX/r7l05h6w4ur9/jkySc\nw6oRArv05IbaqJ+A0iVJZIIPd5iz3q4jhNTpWYpS1/AJPiHoaFpH4/GVBxIh/zoyfdHFxUjs\nVzMLiqwHSoWcYLcXrYnVB0/G9FYDOzRkh5I1F4lGksn27EtiCSEamrKzOlX1KA4KGBSbN8GE\nsJgqgXKMDxV/MVBCBry+BoNeTtUAVUFgl55utXgCVbFQquQEu6xmTlzIyelVdRTLJxI4h80X\nHU2n7EHGMWgTCJe0GInNhqP7sF2nSgjs0tOtHsVixw5KlSuQU2Dn4FhfPKGeKaJ8IonALl9Y\nhu6rSfHj+cpaa/EXA6XiuNdHCOmzI8FOjRDYpbfa7kTEjh2UKhcfZiiq1cht7tvV1soumBAQ\n2OXRbd3tdfpXHKjd2uK8AoEdXFq/108RsrsGgZ0aIbBLby3HDsUTUKpcfKipitt0tYFDTYWx\nsaQYF0Xk2OVRUxX3L6/Z++EdbdfV1xKK7LFV/0N3h9KLAvWSCDnh9XeYqzZRZQ9FgIdjems5\ndmo5hwLISjSZnItEr3XaN/0Kq63s1BHYBQUMis0/PcO8ZUsDIWTmj5GxQEiUpNSZdwCETAbD\n3lj8uoZapRcCqWHHLj0d2p1AKXPxYUnazDCxdQ41HcVi7ERBHbBb+IRwzh9UeiGgXpgkpnII\n7NJDg2IoaePyMLFN9TqROTi5lZ0qOp5g7ERBHai1EkKOeXxKLwTUq9/jYyiqx2pWeiGQGgK7\n9FiMFINStulhYuu0NF2j06nlKBY7doW002Kq0jBHEdjBJYiSdGolsNNiMiAdQq0Q2KWnoxlC\nSBw7dlCaXHzYqNXU5tZH1GlgcRRbCRiK2mOrPuvj5QAa4CLD/mAwIeAcVs0Q2KWnYyiCHDso\nWeN8qM1oyDET3qHXr8TiakhIwFFsoR2wW5OSNLDsV3ohoEarCXZ2tCZWLwR26SHHDkrXYjQW\nSAibmxJ7IaeBlQhZSDV7qshwFFtoB2othJBjSziNhRT6vT6WobdXG5VeCFwSArv0WIYhyLGD\n0uTiw4SQthwqJ2TqKYzFUWyh1XP6BoP+qGdF6YWA6sRFcWiF311Trd1sU0woAvzZpLc6eQI7\ndlCCxnOunJA5OT0hqiiM5YUkIcSoQWBXQAfsVnckNhOKKL0QUJfBlUBcFJFgp3II7NLT0BRN\nUcixg1Lk4sMUIVuMue7YqWeqWDAhaGlarlWHAlk9jUVtLLxSv0fuYIcEO1XDwzEjOppGjh2U\nIhcfqjfoc29M4OBYihB3WBWBHc5hC21PTbWGotD0BC7S7/WZtZrcTwCgoBDYZYRlaOzYQclJ\niOJ0MNKej6ewjqatrE4VR7EJwaRFA63CMmiYnVbTgNefwHMP1gQTwkggtNdmwbQ5lUNglxEd\nTSHHDkrOZDAiSFIuMycu5ORU0cqOTwjodVIEB+zWaDJ5ZoVXeiGgFieW/aIk9dmRYKd2COwy\nwjIMjmKh5MgzJ3KZEnshB8cux+KKl4cHBcGEyonC22+3EEJwGgvr1kbEIsFO7RDYZURH04r/\nPAPIltzrpCNvO3Z6iZBFRTftEqIYS4rIsSuCrWajRac9hqYnsGbA66vj2EaDXumFQBoI7DKC\nHDsoRS4+xDJ0Q54exGpoZYexE0VDUaTPZhkNhJZjcaXXAsrzxuJTwQganZQEBHYZYWkaOXZQ\ncsb5cJvRQOcp1VnueOJWOLBLEnQnLpYDtRZp7QAOKly/1y/hHLZEILDLiI5BuxMoMf54whuL\n56UkVubg9ISQBUULYzFPrJgO2C0U0uyAEELIgMdHCNlTgx27EoDALiM6GkexUGLGVisn8pNg\nRwhxyq3slN2xE3AUWzw1rK7NZDjmWZGUXgkobmDZ32o02PU6pRcC6SGwywjL0KIkCRKeb1Ay\n1ion8rZjxzJ0tU6r9FGsQDBPrIj2260rscRYIKT0QkBJM6HIQiSGBLtSgcAuIxgXCyXHle8d\nO7Layg5HsRUEs8WAoNFJqUFglxEdQxNC0PEESoiLD9v1umqdNo+v6eD0nlhcwWkEqIotsl1W\ns55hjqLpSWXr9/poitpVY1Z6IZARBHYZkXfsUD8BpUKUpMlgOI+VEzInx0oSWYwq1v+Cx45d\ncWlpeleNeXAlEEkmlV4LKEOSyMnlwLZqI/7dlQoEdhmRd+xQPwGlYiYcjSXFfA0TW7fWyk6x\n09i1o1jMii2eA3aLIEqnlgNKLwSUMcoH/fEEEuxKCAK7jGDHDkqLK5DPYWLrnAY9UbQwlk8I\nGprSMwjsimd1ttgSTmMrFBLsSg4Cu4ywDIonoJTkd5jYOqfSwyf4hICS2CJrNRrqOBbd7CpW\nv8eno+mdFpPSC4FMpX9EJpPJp556Khq93OFLOBwmhIjle1KJ4gkoLS4+pKGolqqCBHYK7tgF\nBQGJPsW332Z5amZhPhKt5zAntLIIojS4wvdYzfLuBpSE9I/I559//tZbb83ktc6cOZPzelRq\n9SgWgR2UCBcfajEaNHR+homt0zNMtU7rDiuWY8cnhDo9q9S7V6wDtZanZhb6Pf6bmhHYVZYh\nHx9NJpFgV1rSB3bXXnvtL37xi8vv2H3wgx/0er3d3d35W5i66JBjB6UjLCQXIrHXNxSkN4GT\nYxeiSh7F5r3UF9Lqs1loijrqWbmp2aH0WqCo+r0+QkifHQl2pSR9YMcwzC233HL5r/n4xz/u\n9Xppumy3atdy7FDwDyXAxYekAlROyBwcOxIICZKkofK8HZiWIEqxpIij2OIzaTXbqo39Xn9S\nkpii/7mDgvq9/ioNs9WMT1OlpGxDsfxCuxMoIXLlRN57ncicnF6UpCUl0uzQxE5BB+yWYEI4\n5w8qvRAonkgyOezj99iqaUTzJQWBXUbQ7gRKiDxMrEBHlg7lCmN5AYNiFYOmJxXo5HJAkCQ0\nOik5COwywqIqFkqHiw+btBq7XleIF1ewMBaDYhW002IyaTVoelJR+j0+QggqJ0oOAruM6GiG\noI8dlAKJkIlguKNgOTEOTk8UGj6Bo1gF0RS1p6Z62B+U/xSgEvR7/VZW22IsSFIHFA4Cu4zo\nGIogxw5KwUIkFkwIHQUrHVVwx04OKYyYJ6aQA7UWUZLkOQRQ9vzxxHgwtM9mQXpdyUFglxHk\n2EGpkBPs2gpTOUEIMWgYk1aDo9gKdMBuJYQc8yDNriL0e/2ShEliJQmBXUZYhiHIsYNSIAd2\nhduxI4Q4OT2OYiuQg2ObqriXUT9RGeQOdnuRYFeCENhlRN6xQ44dqJ+LD1MUaTVyhXsLB8cu\nReNJSSrcW6S0dhSLwE4xB+yWpWh8MhhReiFQcP1ef1MVJ1fBQ2lBYJcRDU3RFIUcO1A/VyDU\naOD0TAET0Zwcm5SkpWi8cG+REo5iFSc3PcFpbNlbiMTmw1Fs15UoBHaZ0tE0cuxA5eKiOBOO\ndhQswU621squ2KexvCBoKKqgMStc3l5btZam0fSk7B2XJ4khwa40IbDLlI7Gjh2o3QQfFiWp\nQMPE1jk5PVGiMJZPCEatBjV6CtIzTLfVdHLZn8DDsKz1e/wURfbUFGTeNBQaArtMsQyNHDtQ\nuYIOE1vnNCjT8SSYEJBgp7gDdkssKQ6uBJReCBSKRMiJZX9IzbpOAAAgAElEQVSnqapap1V6\nLbAZCOwyxTIMqmJB5YpQEkvWduwUOIpNCEiwU9x+u5UQcnQJp7Fla4IPL8fie3EOW7LwlMxU\nHnPsggnh17OLk8GIldUectoKNNMTKpCLD3MMIwdehVOlYYxKtLLjE8k2Ex5ZCus0V9lY3VGP\n7/1KrwQKpN+LSWKlDU/JTLEMHRTyMEvnnD/46WNDvnhC/uUPx2beu7Xlne1Nub8ygIsPtZkM\nVOHT0Jwcu1DcwE6QpGgyadTgkaUwipA+W/Vv5pa8sbiNLcg8YlBWv9evoaleJNiVLBzFZoql\n85Nj98CpkfWojhAiStI/n5+cCIZzf2WocMuxuC+eKPQ5rMzBsUuRmFjEVnbodaIe++0WiZDj\nqI0tR0lJOrns31Ft4lB+XrIQ2GVKx+ThKHY+Et0Yw0kS+ROauUPOxvkwKeQwsQs5Ob0gSZ4i\ntrILYlCsauy3WylC0PSkLJ3zB8NCss+OBLsShsAuUyxN597uJCKkfoWwkMzxlQHG+BAhpDgp\nm06u2IWxmCemHlZW22muOu7xFX34CBRcv9dPkGBX4hDYZYplaFGShNyeZA0GlmVS3PM2YzF2\nWaC8FXPHrvg9ijFPTFX2262+eGIkEFR6IZBn/V4fxzA7LCalFwKbh8AuU7p8jIvVM8xbtzRe\ndJFl6H3Y94acjfGhOo4tzp6Wo+g9iuXAzozATh3WZovhNLZMxEXxp5PzXzpx7tRyoLFKzxSh\nAgsKBoFdpnQMTQjJvZXde7pa9tas7nJzDNNproolxXsHzgkiTjVg85KSNBmMFKdygqwdxRaz\nMBY7dqrSazVzDIM0u/Lgicbf+/uBrw+5np33iJI0Ggh9+tgQfiSVLgR2mWJpmhCSe/1EXBRH\n+VCrkTt8/auevOFV33n1nr9sre/3+h4YHME/I9i0qWAkIYqFnjmxzqTVVGmYYu7Yyc2GkGOn\nEhqa2l1jPrMSQH5wGfju+cnZ8CvSKl5eWnlqZkGp9UCOENhlSs6Ni+Yc2L3g9vAJ4ZaW+ioN\nI292f3hH28G6mmfnlr57biLXVUKlGg8WL8FO5uD07iLm2AUTSUII+tipx4FaqyBJJ5b9Si8E\ncnU0VVuGl9GroWQhsMsUm6ej2MPTbpahb2ioXb/CUNTde7Z1W02Pu2afmJjL8fWhMrmKWBIr\nc3LsYjRWtLpIVMWqDdLsykbKk6jcu0CAUhDYZSovxRMuPnRmhX9dfe1FqUIsQ9+/v7vdVPWt\ns+NPzy7mtNBNkQgZCQRfcHuG/cFidp2FfHEFQlqabq7iivaODo4VRMkTK1IrOz4hMBTFadDH\nTi2aq7h6To/Argx0mlN8IOzErMuShY+/mcpL8cThqQVCyC0tzo2/VaVh7t+/8yMvnXpocNTK\n6g4UsU7WHYn975PnB1cC8i87TFWf3t2FCbalxcWHtxi5YtayrXc8qdUXY64UnxCMWg1K9VRl\nn93y5LR7PhytNxR2PDEU1Ds6Gk8fC1z4gb5ap33LlgbFFgS5wY5dpuQdu1x2pyPJ5DNzix2m\nqu3VxpRfYNfr7t+/k9Mw9w4MF61BlETIPf3D61EdIWSMD33m+NncD52haIIJYSkaK3Is7ixu\nxxM+IeAcVm1wGlsefjvnkQip5/Q6mq7SMIectm8d3GUvygc2KAQEdpnSM3JV7OZLwJ6b84SF\n5K2tKbbr1m0xGr64b4cgSp8+dnY+XIzM9NFAaGMQuRiJyf3HoSSMB8NScSsnSNGHT4QEoQrn\nsCrTZ6tmKApNT0ra4Wn3M3NLVzts//7afU/d8KrD17/qnr3bsQVb0hDYZSr3HLtfTLkNGua6\n+trLf1mv1fy5PVt98cSdx4Z88cSm3y5DnmjqH8xLl7gOKjQWKHblBCHEadCTIg6fwI6dChm1\nmh0WU7/Xl+NIHlDKGB/65tB4Paf/ZG8nRQiNvsRlAYFdpuQcu00fxZ7zB0cCwesaag0Z7Dpc\n7bDdtrN9JhT59LGhSA57hJmw6rQpr9fq2YK+L+SRiw8TQorWxE5m1moMxWpll5SkiJBEYKdC\n++2WsJA86+OVXghkLSwk7x04JxLp7r3b0Pq7nCCwy5TcoDi+2Wbcv5hyE0Jubr7cOeyFbmlx\n/s/O5nP+4OeOn00ULN1tyMc/MDi68bqN1WEIdAlx8SGLTlvDFjsnxsGxxRk+wScECb1OVElO\nszu6hNPY0vPVM2MzocgHtrdtu0TaN5QoBHaZYnPIsQsJyefnPTstpq5UVeWX8jddLX/ZWj/g\n9T9wajTvBx2xpPjdc5MffWlwNhS5taW+12r+8+9RREPTCcyTKQWnVwLfHZ487w+atdrcx6Jk\ny6FnFyLFaGUXRBM7tdpebTRpNcc8aGZbYg5PuZ+ZWzpYV/NXrfVKrwXyDA/KTOWSY/f07GI0\nmbw5VZeTy/vIjjZvNP7c/FKtXvf+7Vs28dYpnVoOPHR6dCYU6baa7ujpajVyEiFjgdBsOOrk\n2DMrgW+cHb//1Pn7+nYg4ULNvj7k+tnkvBxWTYXC7/l9/4NX9DQWMevZadAnllaW43FbgTcL\n0Z1YtWiK6rNZfrfg8cUTlkvkdYDauPjwt4bHHRz7qV1deMiXH+zYZSqXHLsnp91GreZapz3b\nb6Qp6jO7t/ZazT8en/2/43kYShESkg+fHvvYnwaXorH3bWv96pW9rUaOEEIR0mmuOuS0bas2\nvnlLw/UNtUcWln+CMRgqdtTj++laVCdzR2JfH3IVcw1rrewKfhorB3ZIA1Kn/XaLJJEB1NGX\niEgyed/AcFKU7tqzDR+WyhICu0zJOXabOO0aXAmM8+E3NNbJh7lZvy9Df3Hfjlaj4dFz48/N\nL23iFdb9YcH7N7/rf3Lavd9u+Zdr+t7Z3nSpGqjbujtaqrjHhieGkBOtVi8uLm+8eMzjK1xG\n5kZrrewKXhgbFDAoVr0O1FoIIUdxGlsiHhocnQpFPrijbafFpPRaoCAQ2GWKZRiyqckTT065\nqWzKJjYyaTUPHNhpZ3X3nxo57t1MkvJKLHHvwLm7+4fjonjnrq4HDnTLey2XYtAwn+/brqHp\neweGi9ByBTYhKqRI9xQlKVrETLvVVnbhgu/YIcdOzer0bKvRcMzjQ1qu+v1iyv38vOc1ThtS\n68oYArtMsZvKsQskhBfc3l011fKJ56bV6dkvH+jmGOae/qyHUrzg9vztHwZecHsO1tV87+q9\nb2isy+S7thgNd/R2LEXjXzhxDtNjVWhLquYmdXq2mNFP0Y5iAwjs1G2/3eKJxieDYaUXApfj\n4kPfHh53cOzHezqVXgsUEAK7TGloiqaobHPsfjWzEBfFW1ocuS+g1Wi4f//OpCTdeXRoJhTJ\n5Fu8sfhd/WfvHThHU+Sevdu/tG9HVlNiXldfe0uLc8Dr/7fR6c2uGgrljU0O84ZA512dzcVc\ng0Wn1TNMMY5iEdip2wE0PVG9SDJ578A5USL37t2Of0rlDYFdFnQ0nVWOnUTIk9ML1TrtNQ5b\nXhaww2K6e882PiHceWxoORZP99bud/+u/8jC8iGn/f9c03fIuZk1fGRH27Zq47+OTb+8hAQa\ndUlKEiFES9PyXnJzFfe5PVtvas7DR4isFKeVHapiVW53TTXL0Gh6omaPnB6bDkU+uH3LVnSt\nK3d4UGZBR2e3Y9fv9c2EIu9sb9LSeQugD9bV/EN3xz+dHv3M8bMPX9GTco7FXDj60OnRE16/\nk2M/v3e73EF0c7Q0fe/e7X935MSXTp5/7NV7nJfNzINi+sqZsUBCuGfv9mscNQlR2lxpTu4c\nHHty2S8RUtCmCXxCoCmKw6xYtWIZusdiPrkciCVFpf4qwmX8fGr+N3NLh5y2v0RqXQXAv8As\nsAydVY6dXDbxxqaMctoyd1Oz492dzef9wbv6z7r40OFp9xMTc2dWeEJIUpKemJh77x8GTnr9\nNzc7//nqvblEdbI6jv3U7q1BQbhvYFhA12J1+PXs4u/c3usbag85bTRFKfij1MmxsaS4ctn9\n49wFE4JRw6Dhlprtr7XERXFwJaD0QuBiLj706PBEg0F/R2+X0muBYsCOXRZYhsm8KnY5Fj+y\nsLzPbmmqyqlsIqV3d7UEEsJPJ+ff94cT66HWXps5lBDPB4KNBv0dvZ27a/I2E+xVtdZ3tjf9\naGzm0eHxj+xsz9fLwuZ4Y/FvnR23sTo1/Fms108UdKAZnxBwDqtyB+yWxwg56lnJ/cMk5NF6\nat3de7ZVYc+7MmDHLgtZ5dg9NbMoSNIt2U+byNAuq5kQcuEG2oA3MBIIvrO96fvX7M1jVCd7\nb1frPrvlJ5Pzz8zl1EsPciQR8uDgKJ8Q7ujtVEOss9bKrrBpdkFBQHdilWszVdlY3TEP6ifU\n5WGk1lUeBHZZYBk6wxw7SSJPTbttrO6qupoCLeaFBe/Giza97n3bWvOY0reOosind3XZWN1X\nz4xNZVaTC4Xw5LT75aWVm5odV9ZalV4LIeut7ApcGBtMILBTO4qQ/XbLOB/2RAt7Lg+Z+/nU\n/LNzS4ecdqTWVRQEdllg6Uxz7F72rLgjsb9odjCXGO2QO3+qvsHBRIqmtflSw+ru2rMtlhQ/\n3z8cTRbwjeBS5iPRR89OODn2g9vblF7LqiLs2ImSFBaSatiehMvDCApVGQkEv3VWTq1D17rK\ngsAuCzom06PYw1NumqJuaipg74mGVLPeCz0AfleN+X9ta50Ihh85PVbQN4KNJIk8cGokKiY/\n2duVshpaERZWyzJ0QTueBIWkRIgJ88RUr89moSiC01g1CAnJewfOEULu2YvUuoqDZ2UWdHRG\nR7FL0fhLSytX1lrrCtkc5NaW+l/PLAqvnAnxli0NhXtH2dvaGodW+Gfmlnqs5sJlEMJG/29i\n7tRy4C1bGvbY8pxAmQuKEIe+sK3sVpvY6fCwUjuLTttlNh73+CSJFOysohzERfHnk+7TKwFC\nSG+N+dYWpy7f+TNfOTM2F47e1t3eZUZqXcXBjl0WWIYWJSmZbr7Wf027xUKWTci6zFX/uH/n\n+r6dSav56M72G/PdWmUjipBP9HY2GPTfODt+zp/dcDPYtKlQ5Psjkw0G/d9ubVF6LRdzcKw7\nEi1cIxyMnSghB+yWQEI4n+XYw4oSSAjvP3Li28Pjv1/w/n7B+62z4+8/clL+9JIvP5tcTa17\nUwtS6yoRArssyC3+L38am5Skp2YW6jj2isLX/O+3W/7tNfv+/dC+712994nrrihaeqxRq7mv\nbztNkXsGhgN5fR5BSklJeuDUSFwU79zVxTGqO1VxcvpYUkyZ9JkX8s88I45iS4Hc6wRpdpfx\no7GZyeAr6s8mg+Efjs3k6/XH+NBj55BaV9EQ2GVBx6QP7F5cXPZE4zc3O+miHEVQFGkw6NtM\nBk1xTz7aTVUf2dG+GIk9cGoEPYsL7XHX7Fkf/462pl6rWem1pOA0yIWxhTqNXQ3stKqLaGGj\nbqvZoGEwNPYyjqfKQez35ueOhYTkPf3DkkTu2bsdqXUVC4FdFlZ37C6bZnd4yq2hqLxPm1Ch\nm5odb2ise3Fx+XFX3j5rwkZjfOgHo1NtJsPfdDUrvZbUHJyeELJQsI4nQUEO7LBjVwI0FLXV\nbDyzwj86PPHbeU/axJUKFBFTtBRw8eHPHBv697GZk8v+rCaSX+ShwZG5cPRDO9q6zFU5rBFK\nG56VWZB37C4zfGI+HD3m9b3GYbMVsgu/enysp8PFh/75/ORWs3Ef2s0XgCBJDw6OShL5ZG9X\nIdoT5sVaK7uC7djFBUKIGYFdKfjByJQ8O/g/x2cJIe0mw/37u+36ingepjURDP+Ha3Y+lOIj\nkFWn7ff6X1paIYQwFNVUxfVazT1W066a6rQTugVJWoklbKz251PuF9zeQ077rShrq2x4VmYh\nbY7dk9MLkkRurph/VDqavnvv9g8cOfGPp84/dtUePL7z7gcjU+f9wfd0tWxTcdf49aliBXr9\ntaNYPKzU7tRy4Aej0xdecfHhrw257uvbrtSSVGI0EPq/E3PPzi2JktRlNk6HwtELfo5UaZhH\nruypN+jHAqHBlcDpFf7ksv/JafeT025CiI3V9VjNPVbT1mrjDovpwqwbPiF859zEr2cXBVHS\n0ZQgEaTWAUFglxV51PqlehQLovTLmYWmKq7PVkF7V40G/R29XfcNDH/x5Ll/uqKncA2ZK9Cw\nP/i4a3ZbtfG/dTQpvZbLqWF1Opp2hwt8FIviCdX7fapxOC8tLguipKEr9Mlwctn/w7GZYx4f\nRZGr62z/vaNpa7VxNhz9/vnJU8sBQsjuGvPfbm2V+xtsrTZurTa+ZQshhEyHImd8/OnlwBlf\n4HduzwtuDyHEoGG6LaZuq7nHatpebfrM8aEzK7z8RnFRIoTUG/RIrQM8K7OwWjyRKkOCEPK7\nBa8vnnhHe2OlPcAOOW1v3tLwxMTc985P/t22LUovp0zEkuL9J88zFPWpXV0qD5cpQuo4tnBH\nscGEQFEI7EpAMFWNvCBJvniiArfzX15a+eHYzOBKgKGo6xtq/1tHU6vRIP9Wo0F/155tl//2\n5iquuYq7sbGOEBJICGdWAmd8/OBy4NRK4KjHRwihKErakMJ43OObj0TrucJ2qgeVw7MyC5c/\nin1yyq2l6Tc0ln/ZxEbv375l2M//2DXbbTG/2lGo8bgV5XvnJ6dCkQ9s37L+w0DNnBy7vnOQ\nd3wiadQUuewbNqPFyKW8/rd/GLi23n5jY90Oi6nISyo+SSK/X/D+cGxmJBDU0vTNzc53tjfW\n5zYTyKzVHKyrOVhXQwgRJOm8Pzjk45+eXRwNhDZ+8VQwgsCuwiGwy8LqUayYos5rKhQ5uey/\nrqG2Wqct+rqUp6Gou/dsf/+REw8MjnyW3roYiRFCuq3mdlMJBCUqdGo58MTkXI/V/NYtjUqv\nJSMOjj3m8fnjiUL8/ecTAhLsSsIbmxz/OT53UUfDqx22hUj08JT78JS71Wh4Y1Pd6xtqa8qx\nvCwpSc/NLf3INTsZDOsZ5i1bGt7e1pj3rUoNRe20mHZaTDWs7osnzm38AuxtA/4GZEGe+hJP\npjiKPTzllgip5BFbtXrd53Zv/cTRoU8fG5KvUITc3OK8bWd7cVr6lY1oMvng4IiOpj/Z21kq\nd26t40msMIFdwlyRn5dKjkWn/cqVPV8bcp3w+iVCqnXad3c2v6m1niJkMhh+enbplzMLjw5P\nPHZuoq/GcnOL49V1tvLIvRNE6bn5pX8fm5kJRQwa5s2t9e/saCp0b4R9tmqOYSKv/Hlk1+u2\nWdRbaAXFgcAuC2s5dhcfxcaS4tOzi61GrkeV/WOLZjIUkciftzMlQg5PuduMhqKNxCgP3z47\nMRuO3tbd3lSV+mBLhdY7nmwtQPUunxAaDSVzKypcq9HwT1f0hIVkUBDq9OyF19+3rfW9W1sG\nlv1PTi0cWfAe9/pMWs0hp/2WFkdJzDMVJenwtPunE/Nz4Wgtx/5Fk+NtbQ1JSfqv6YX/HJ9d\nisblQPbNWxqKM/6uWqf95K6uBwdHwkJy/cpnd29F1gIgsMvCpXLsfuv28Anhb7paKvzf0/Pz\nnpQXEdhl7pjH9+S0e5/NcmtJDXlcC+zyXxgrSlI4mcRRbGkxaBhDqtpMmqL22Sz7bBZvLP6b\nuaVfzSzKTT26zMYbm+pe31B7UUi0GIlNhSJWVttmNCi+8f+Ns+M/m5yX//98OPq985O/mVvy\nxRP+eMKu131oR9vNzQ59cSf+HXLaeqym38573JFYU5X+dfUX30CoTPhLkAX2Eg2KD0+5WYa+\nvqFWiUWpiC/VtNCVgo0QLT/BhPDg4IhBw3yit7O0PiQ4OT0pTI/ikJCUJMwTKzc2Vvf2tsa3\ntzUO+fhfzyw+N7/09SHXo8MTV9XV3NhUd8BuiSbFR86MPTe3JB8BtJkMn+ztUrCbozcW//nU\n/EUXJ4Nhu173sZ6OGxvrlOofbmN1b9nSoMhbg2ohsMuCjmbIhj52Y3xoyMe/scmBTYUGg34m\nFNl4UZHFlKJvnB1fisY/2dtVl67XvNrYWJ2GpgrRo1juTox9iHIl1wF8aEfbHxa8v5pZ/N2C\n5wW3x8bqqjSaqVB4/cvG+fCdR8/826F9Sv1NGAuEUk5He1dH8y3NlZtaDeqEx2UWdAxFNuTY\nHZ5yk8oum1j3V631R5dWLnr6eWNxPiHgB3NaRxaWn55dPFhXc2MJDhqmKOLQs4UYFxtEYFcB\nWIa+rqH2uobaxUjs17OLv5xZuDCqkwUSwu/c3puaHUVe20IkdmRx+VczCyl/V49uwKA+Kp0+\nqU4bc+wiyeRv5pa6zFXbVTzxqWiurLV+evdWK7tawGhltQfslrFA6MMvntq4kwcX8scTD58Z\nNWs1t/d0KL2WTXJy+kIcxfIC5olVkDqOfVdn8337dqT83ZcWvYsF64N9ERcf/rfR6fcfOfnO\n3x77xpBrOhTZWMCroam9NdXFWQ9A5vC4zALLMOSVOXbPzi2FhSS269a9vqH2tfX26WCEENJs\n5DQUdXjK/bUh14dfPHXP3m0VNWwtK185M7YSS9y1Z1uhWyQUjoNjj3t9ed+dXd2xQ2uuSmLT\n6ShCNp58HllcObJ4zMmxu2qqd9eYd9VUN+Y100OSyGlf4MjC8h8WvHPhKCHEpNXc0Fh3taPm\ngN36x8Xl+0+NJNae/zRFfWB7WwVO1AD1w+MyCyx98azYw1MLBg3zuvpKL5u4kIai2i7oS3xL\ni7OhSn/fwLk7jw59eEcbKmQ3enZu6QW392qH7dp6u9Jr2TzHWseT/AZ2ATmw0+FJVUGsrHa/\n3SIPzlqnZ+gP7Wib4MOnffwzs4tPzy4SQmpYXa/V3GM19VrNXdXGS5UchYXkaCAUF8UOU9X6\nkcK6hCieWgm8uLD8gtvrjcUJIXV69uZm58E66wG7dX2j7tp6+9Zq439Nu2dCUQfHvqGxrtNc\nle//dIA8wOMyCxqaoqg/59gN+4MjgeCtLc6Uhf2wbp/N8q2rdn/22NDXhlwuPnxbd7vKh58W\nkzcW/9qQy6LTlu4hrEzueLIQiXbl9acdduwq0527ur544vyJZb/8Sxur+0Rv5xW1VvmXvnji\nrI8/vcIf9/rkegtCiEWn3WEx9VhN+2yWLrNx/RnzzOziN8+Oy58QNDT1trbG93a1UhSJJcV+\nr++3bu+RBa/cCq7eoH9za/2henuP1ZzyCdVo0GMcNqgfHpfZ0dH0eo6dXDZxE0qiMtBo0H/9\n4K57BoafnHYvRKJ3791eVcHRcCwpPjWzMBYImbSawZUAnxDu7dtuKfHhCk5DQTqeoCq2MtWw\nuoev7Bny8VPBSA2r7a0xcxe0iLPotPLs1PeR1rCQPOvn+z3+wZXAUc/Ki4vL3yWTBg2zo9rU\nZ6/mGObrQ671U11BlH40NuMORyNJ8ZjHlxBFiiJdZuPBWutr62tbLzHrFqC04HGZHT3DyDt2\nwYTw3PzSTospv/sTZcyk1Xx5f/dXzow9NbPw0ZdOfWnfTmepNfXIi5lQ5I6jZy7MAW+p4q5x\n2BRcUl6sHsWG81wYG0wkCYonKpXcDOXyX2PQMHLTY0JIWEieXgmcXA6cWvGfXPYf9/pSfstz\n8x4tTe+1VV/tqLmqrqYsB9dCJcPjMjssTcs5ds/MLcWSIsomsqKhqTt6O1uM3HfOTX7ojyfv\n69tegUPYvjrkuqiybyoUOerxHbCXdmWJndVpKGohmv8dO4qQSt7fhcwZNMwVtVb5xDaWFM/4\nAg8Njm7cRdbQ1E+vuwIpNFCu0O4kOzpm9Sj28JTbpNW81lnC2e5KeVtb4xf6tsdF8eMvn3lm\ndlHp5RSVnNaz8fpLi8vFX0x+0RRVy7HucJ4Du2BCqNJqFB8nBSWHZeg+myXlR0cnp0dUB2UM\ngV12dDQdF8XBlcBEMHxDY508ZAyydbCu5muv6rWy2vtPjXzv/GSqju7lKS6KKfvXR5LJoq8l\n/5wcm/dxsbwgoHICNu0Nqdp9v6Gx9HqAA2QOcUl2WIaOiaJcNnFz0Xugl5N2U9VjV+3usZp/\nODZz78BwLHnxBN6yZNRq6vQpMgs7TOWQqeng2JCQlOtY84WPC0iwg03bZ7Pc1t2+XnhBU9Rf\ntta/o71R2VUBFBSemFmIJUVBFH2xxG/d3t011a1GQ/rvgUur1mkfvKL7y6dGn5tfckcGv9i3\no+y7fVKE3Njk+NfRqQsv1nHsjU3l8CHBya0WxnbmLxQLCoLTUIlFNpAvb2qpP+S0n/XxsaS4\nrdpYj+nVUO6wY5epFxeX/8cLx0cCobgoCqJIqFeMoIDN0dH0Z/dsfd+21pFA8IN/PHnOH1R6\nRYXFJ4Rn5xYZinJwLEUIxzDX1tu/9qre8igOcKy2sstbmp0kkaAgmLFjB7mR26O8tt6OqA4q\nAZ6YGZkORe4ZGBbEP6dHnfT6Hxue+Pud7QquqjxQhLyzvanBwN1/6vxtLw1+orfzuobynOQh\nSeRLJ8/PhqO393Tc3OyMJUUds2H8ZClzrg6fyEOaXUIUfzQ288TkvCSRI4vL3zw7/p6uFiS8\nAwCkhR27jDw9u3hhVCf79exi6kx4yN4hp+3rr9pl0Wn/8eT5H4xMleVt/c65iZeXVm5srLu5\n2UkIYcsrqiOEODg9ydOO3UODoz8YnZbT9QRRemJi7q7+s2X5twIAIL+wY5eRpWh848WwkAwK\nSZwT5UunuerrB3d97vjQD0anZyOxHdXGX88uLkZiDQb9m7fUX1tfW9Jh0PPznv8cn+22mm7v\n6VR6LYVSq9cxFJX78Al3JPabuaWLLg54/UMrfLc1TbtaAIAKh6AkIymT+jmGMeJsKK9q9bqv\nvqr3H0+O/GZ28TdrLe588cTQCX6cD793a6uyy9u0MT704OBoDav7/N7tmnLbp/szhqJq9bqF\nnI9iJ4PhlJtz48EQAjsAgMvDUWxGrm+o02xokXpDYx36puadnmHe3dm88frjrllfPFH89eQu\nkBDuPj6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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 3863 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  IRF4, JCHAIN, NFKB1, IGHG3, IGHG1, IGLC2, IKZF3, BASP1, FAAH2, PIK3R5 \n",
      "\t   RASGRP3, MIR155HG, SLC12A6, SLAMF1, JSRP1, LY9, IGHG2, RBM47, IGHGP, PDE3B \n",
      "\t   S100A9, EIF2AK3, IQGAP2, RHEX, PLEK, FAM30A, BMP6, OSBPL3, STAT4, RGS1 \n",
      "Negative:  CALD1, ADAMTS9-AS2, EBF1, ADIRF, RBMS3, GSN, SPARCL1, IGFBP7, NFIB, LHFPL6 \n",
      "\t   IGFBP5, PTPRM, FBXL7, PRKG1, ZFHX3, DLC1, TIMP3, MAGI2, CDC42BPA, SLIT3 \n",
      "\t   GHR, FRMD4A, MGP, KAZN, TAGLN, SELENOP, NRP1, PALLD, TPM2, RBPMS \n",
      "PC_ 2 \n",
      "Positive:  KCNAB1, MCAM, MYH11, CACNA1C, RERGL, ACTA2, RGS5, RCAN2, MRVI1, TINAGL1 \n",
      "\t   LDB3, CASQ2, PLN, PDE3A, SNCG, LMOD1, CAV1, CCDC3, C11orf96, DGKB \n",
      "\t   RGS6, PHLDA2, NMNAT2, NDUFA4L2, NR2F2-AS1, TNS1, COL4A5, MYOCD, EDNRA, COL18A1 \n",
      "Negative:  CDH11, DCN, SLIT2, CFH, NCAM1, LUM, RUNX2, WIF1, COLEC12, KAZN \n",
      "\t   CFD, TENM4, BICC1, PLEKHA5, ITGBL1, PCOLCE, PTPRD, S100A13, FLRT2, SATB2 \n",
      "\t   SFRP4, BGLAP, GHR, LRP4, COL1A2, CLEC11A, FAT3, SRGAP3, COL8A1, FBLN1 \n",
      "PC_ 3 \n",
      "Positive:  ADGRL4, EMCN, FLT1, SHANK3, LDB2, PTPRB, PALMD, CDH5, VWF, TM4SF1 \n",
      "\t   BTNL9, EGFL7, SOX18, PCAT19, CLEC14A, TEK, CALCRL, TIE1, IFI27, CHRM3 \n",
      "\t   PODXL, CLDN5, GIMAP8, RAMP2, MECOM, SHE, DNASE1L3, GIMAP7, EFNB2, PLAT \n",
      "Negative:  TPM2, KCNAB1, ACTA2, RCAN2, MYH11, SOD3, RERGL, TAGLN, SLIT3, PLN \n",
      "\t   LDB3, MYL9, RGS5, LMOD1, CASQ2, NOTCH3, PDE3A, MRVI1, EDNRA, NDUFA4L2 \n",
      "\t   MAP1B, RGS6, IGFBP5, CRYAB, PRKG1, MT1M, LBH, MCAM, ZBTB7C, CNN1 \n",
      "PC_ 4 \n",
      "Positive:  RASGRP3, TBC1D9, ITGA6, SCNN1B, FAAH2, CARMIL1, JSRP1, ADGRL4, IRF4, EMCN \n",
      "\t   LDB2, ZNF215, DGKI, PTPRB, RAMP2, SLAMF1, PALMD, MIR155HG, JCHAIN, CABLES1 \n",
      "\t   RAPGEF5, SHANK3, CDH5, BTNL9, SOX18, OSBPL3, PCAT19, BFSP2, TM4SF1, VWF \n",
      "Negative:  NLRP3, IL1B, CXCL8, KYNU, LYZ, EREG, PLAUR, EMILIN2, S100A9, DENND5A \n",
      "\t   SIPA1L1, TYROBP, MNDA, ITGAX, OLR1, S100A8, PIK3R5, ANPEP, SLC11A1, ZEB2 \n",
      "\t   LCP2, CSF2RA, LST1, JARID2, BCL2A1, RAB31, DMXL2, ACSL1, MPEG1, C5AR1 \n",
      "PC_ 5 \n",
      "Positive:  WIF1, SFRP4, BGLAP, CPE, SRGAP3, COL13A1, SPP1, MMP16, PTPRD, CADM1 \n",
      "\t   CHAD, NCAM1, IBSP, LRP4, PCDH9, CLU, CLEC11A, MAP1B, S100A13, INSC \n",
      "\t   SERPINE2, OMD, EMP1, ENPP1, CDC42EP5, SLC44A5, SLIT2, SDK2, GAS7, PDGFD \n",
      "Negative:  CP, NCAM2, CCBE1, EYA1, LEPR, TMEM132C, PAPPA, ANGPT1, NRXN3, TRABD2B \n",
      "\t   CHL1, ESM1, NAV2, VCAM1, NLGN1, CXCL12, CHRDL1, BMP5, TNFAIP6, DCLK1 \n",
      "\t   TMEM108, ABCA8, PID1, CNTNAP2, ADAMTSL3, EBF3, ABCA9, LINC00640, LINC01725, P3H2 \n",
      "\n",
      "19:31:02 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:31:02 Read 8674 rows and found 30 numeric columns\n",
      "\n",
      "19:31:02 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:31:02 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
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      "*\n",
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      "*\n",
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      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:31:03 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a427c1725\n",
      "\n",
      "19:31:03 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:31:05 Annoy recall = 100%\n",
      "\n",
      "19:31:06 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:31:07 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:31:08 Commencing optimization for 500 epochs, with 360512 positive edges\n",
      "\n",
      "19:31:08 Using rng type: pcg\n",
      "\n",
      "19:31:17 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:31:17 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:31:17 Read 8674 rows and found 50 numeric columns\n",
      "\n",
      "19:31:17 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:31:17 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:31:18 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a79f01f75\n",
      "\n",
      "19:31:18 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:31:20 Annoy recall = 100%\n",
      "\n",
      "19:31:21 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:31:23 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:31:23 Commencing optimization for 500 epochs, with 357510 positive edges\n",
      "\n",
      "19:31:23 Using rng type: pcg\n",
      "\n",
      "19:31:32 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 8674\n",
      "Number of edges: 309313\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9021\n",
      "Number of communities: 28\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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d7Ao2IHAFiNYAdYx9fG27BzQqjYAQBa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\nsAMAAADIEQh2AAAAADni/wNnj6hynaWuUAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 2891 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  NNMT, CALD1, KAZN, FSTL1, DLC1, LHFPL6, COL1A2, CXCL12, DCN, RBMS3 \n",
      "\t   NAV2, GHR, FBN1, EPAS1, EPS8, COL6A2, FBXL7, PTPRG, EGR1, LUM \n",
      "\t   LIFR, CYP1B1, EBF1, ENAH, CDH11, PTPN13, ABCA8, LAMA4, COL6A1, APOE \n",
      "Negative:  AREG, MCTP2, ITGA4, CD69, HIST1H1D, KCNQ5, AFF3, TFRC, PCLAF, HLA-DPB1 \n",
      "\t   HLA-DRB1, HLA-DPA1, HMGB2, TYMS, ZNF804A, ATP8B4, MED12L, EREG, XYLT1, RAB11FIP1 \n",
      "\t   RGS1, CRYBG1, GPR183, CENPF, PLEK, HIST1H4C, SSX2IP, AC016205.1, KLHL6, PIK3R5 \n",
      "PC_ 2 \n",
      "Positive:  APOE, CHL1, CFD, DCN, ABCA8, LUM, BMP5, NCAM2, TMEM132C, BICC1 \n",
      "\t   TMEM108, EYA1, LRP1, KAZN, CA2, COL1A2, PTPN13, PAPPA, ANGPT1, CP \n",
      "\t   FBLN1, PDGFRA, CHRDL1, CXCL12, PCOLCE, RARRES2, BMPER, ABCA6, PCDH18, FGF7 \n",
      "Negative:  ADGRL4, LDB2, EMCN, PTPRB, VWF, TM4SF1, RAMP3, CXorf36, PALMD, ANO2 \n",
      "\t   CALCRL, TM4SF18, FLT1, PLAT, PLVAP, NOSTRIN, RAMP2, CLEC1A, ACKR1, CYYR1 \n",
      "\t   PCAT19, AQP1, LMCD1, PCDH17, ARHGAP29, DNASE1L3, TEK, PODXL, SLCO2A1, GALNT18 \n",
      "PC_ 3 \n",
      "Positive:  PIK3R5, RBM47, ITGAX, C5AR1, FCER1G, TYROBP, TLR2, NLRP3, FMN1, FCN1 \n",
      "\t   S100A9, PLAUR, BCL2A1, CSF2RA, GPR183, SLC11A1, BASP1, RAB31, SLC43A2, FPR1 \n",
      "\t   SERPINA1, RNF144B, S100A8, LYZ, IRAK2, AQP9, FGD4, MPEG1, HCK, MNDA \n",
      "Negative:  ANK1, KLF1, GATA1, TFR2, KCNH2, HBD, EPCAM, FAM178B, MPC2, SLC25A21 \n",
      "\t   UROD, RYR3, PCLAF, PRDX2, KIAA1211, AHSP, SMIM1, CA1, KEL, HBB \n",
      "\t   RHAG, SSX2IP, APOC1, PTH2R, GAD1, XACT, PIP5K1B, PNMT, BLVRB, TYMS \n",
      "PC_ 4 \n",
      "Positive:  NLRP3, IL1B, LYZ, CXCL8, EREG, KYNU, AC020656.1, FCN1, RBM47, PLAUR \n",
      "\t   CSTA, CSF2RA, EPB41L3, MNDA, SERPINA1, S100A9, FGL2, ITGAX, FCER1G, OLR1 \n",
      "\t   VEGFA, C5AR1, TLR2, SLC43A2, CD163, S100A8, TYROBP, AP003354.2, HBEGF, CD300E \n",
      "Negative:  BACH2, LTB, IL32, IL7R, CD79A, CD247, TC2N, CAMK4, TRBC1, MZB1 \n",
      "\t   CD27, CD69, POU2AF1, VPREB3, IGHM, BICDL1, LBH, CCL5, GZMA, THEMIS \n",
      "\t   RAB30, BLK, LINC00426, ARPP21, PAX5, NELL2, CD24, IGKC, LEF1, LY9 \n",
      "PC_ 5 \n",
      "Positive:  CPE, WISP2, NDUFA4L2, CRTAC1, FAT1, SDC2, PRRX1, BGN, MAP1B, ZNF385B \n",
      "\t   RERG, LRRC15, COLEC12, ZBTB7C, OGN, ENPP1, NBL1, DPT, CRYAB, PLA2G2A \n",
      "\t   WIF1, BGLAP, PDPN, IBSP, HTRA1, SOD3, COL13A1, TEX41, CLU, PRG4 \n",
      "Negative:  NCAM2, TMEM132C, LEPR, CHL1, PAPPA, CHRDL1, CP, FMO2, EYA1, NAV2 \n",
      "\t   NAV3, NRXN3, IGFBP2, P3H2, CXCL12, COL28A1, LPL, ANGPT1, BMPER, LAMB1 \n",
      "\t   TMEM108, WASF3, OSBPL10, VCAM1, BMP5, C7, RBP1, LINC00640, FMO3, ESM1 \n",
      "\n",
      "19:35:07 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:35:07 Read 4457 rows and found 30 numeric columns\n",
      "\n",
      "19:35:07 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:35:07 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:35:08 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a39ba247e\n",
      "\n",
      "19:35:08 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:35:09 Annoy recall = 100%\n",
      "\n",
      "19:35:09 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:35:11 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:35:11 Commencing optimization for 500 epochs, with 174622 positive edges\n",
      "\n",
      "19:35:11 Using rng type: pcg\n",
      "\n",
      "19:35:16 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:35:17 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:35:17 Read 4457 rows and found 50 numeric columns\n",
      "\n",
      "19:35:17 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:35:17 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:35:17 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a414ad242\n",
      "\n",
      "19:35:17 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:35:18 Annoy recall = 100%\n",
      "\n",
      "19:35:19 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:35:20 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:35:20 Commencing optimization for 500 epochs, with 175138 positive edges\n",
      "\n",
      "19:35:20 Using rng type: pcg\n",
      "\n",
      "19:35:26 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 4457\n",
      "Number of edges: 137896\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9141\n",
      "Number of communities: 24\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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y8lwS5vsFUMAAAMu7QEO6VEhIknAABg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\nAAAAADgCwQ4AAACAIxDsAAAAADji/wN6Bc2OyB5VqgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 1486 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  S100A9, LYZ, ATP8B4, RHOH, PIK3R5, S100A12, PLEK, KCNQ5, HMGB2, BCL2A1 \n",
      "\t   S100A4, FCN1, AREG, AC020656.1, LTF, LCN2, HIST1H1D, PCBP3, RETN, CXCL8 \n",
      "\t   PCLAF, MED12L, MMP9, RNASE2, MPO, FAM19A2, CAMP, TYMS, ACSM3, SIPA1L1 \n",
      "Negative:  CALD1, NNMT, FBXL7, KAZN, EPAS1, APBB2, RBMS3, CDH11, GHR, DLC1 \n",
      "\t   FSTL1, ERRFI1, FOXC1, FERMT2, SOCS3, CXCL12, ANGPTL4, EBF3, IGFBP5, MAPK10 \n",
      "\t   CHL1, COL1A2, TMEM108, DCN, EBF1, RND3, ENAH, BMP5, KITLG, NCAM2 \n",
      "PC_ 2 \n",
      "Positive:  MZB1, DERL3, SLAMF7, FKBP11, HERPUD1, CD27, POU2AF1, RAB30, SEC11C, TNFRSF17 \n",
      "\t   IGHA1, IFNG-AS1, SPAG4, LINC00513, SSR4, SLAMF1, JSRP1, CHL1, PRDX4, COBLL1 \n",
      "\t   SCNN1B, BMP5, NAV2, LINC02384, TMEM132C, TMEM108, EYA1, BFSP2, SSPN, CARMIL1 \n",
      "Negative:  ADGRL4, LDB2, TM4SF1, PCAT19, PTPRB, CD36, EMCN, PALMD, FABP4, FLT1 \n",
      "\t   DNASE1L3, TEK, BTNL9, EFNB2, CAVIN2, CALCRL, KDR, ARHGAP29, KIAA1217, ROBO2 \n",
      "\t   PCDH17, TGM2, NOSTRIN, PLAT, ADGRF5, SASH1, TM4SF18, GNG11, MRC1, THBD \n",
      "PC_ 3 \n",
      "Positive:  DUT, PCLAF, TUBB, TUBA1B, TYMS, RNF130, HMGB2, FAM19A2, PRKAR2B, ACSM3 \n",
      "\t   HIST1H4C, CENPF, HIST1H1B, PCNA, SOX4, NME4, KIAA1211, CENPP, UROD, KIF20B \n",
      "\t   KLF1, CA1, TOP2A, GLRX5, ANK1, KCNQ5, DIAPH3, CKS1B, XACT, TPX2 \n",
      "Negative:  TBC1D9, MZB1, DUSP5, DERL3, SLAMF7, RAB30, FKBP11, SEC11C, POU2AF1, HERPUD1 \n",
      "\t   RASGRP3, PELI1, IFNG-AS1, CD27, TNFRSF17, PRDM1, ADGRL4, SLAMF1, CADPS2, LDB2 \n",
      "\t   LINC00513, RAPGEF1, SPAG4, SSR4, MTUS1, JSRP1, ST6GAL1, IGHA1, RAPGEF5, SCNN1B \n",
      "PC_ 4 \n",
      "Positive:  S100A9, TMSB4X, C5AR1, PIK3R5, FCN1, BCL2A1, S100A12, SLC11A1, G0S2, DOCK4 \n",
      "\t   LYZ, ITGAX, IER3, ARHGAP26, OLR1, AQP9, ANPEP, SIPA1L1, SLC43A2, MMP9 \n",
      "\t   AC020656.1, LUCAT1, TLR2, PTGS2, GK, CLEC7A, CSF2RA, CD93, KYNU, CXCL8 \n",
      "Negative:  PRDX2, CYTOR, SSR4, PRDX4, HIST1H4C, FKBP11, NME4, SEC11C, ANK1, NEDD4L \n",
      "\t   MZB1, DERL3, TMEM14C, REXO2, RYR3, FAM178B, XACT, KLF1, CA1, HBD \n",
      "\t   UROD, ZNF385D, GATA1, APOC1, ATP5IF1, KCNH2, RAB30, AHSP, SYNGR1, KIAA1211 \n",
      "PC_ 5 \n",
      "Positive:  CRTAC1, PRG4, ZNF385B, HAS1, HTRA1, MEDAG, FN1, PLA2G2A, CLU, TMEM196 \n",
      "\t   GFPT2, FGF10, SCARA5, GPR1, THBS4, EPB41L3, LRP1B, DEFB1, ITGB8, SEMA5A \n",
      "\t   CDON, COLEC12, ZBTB7C, PDPN, FAP, CLIC5, GPRC5A, TPPP3, SDC2, PRRX1 \n",
      "Negative:  LPL, NAV3, NRXN3, LEPR, CHL1, TMEM132C, P3H2, EYA1, NCAM2, EBF3 \n",
      "\t   ADGRL2, BMP5, PAPPA, ESM1, THSD7B, FAM241A, ADAMTS9, FMO2, TMEM108, OSBPL10 \n",
      "\t   NR2F1-AS1, APBB2, TENM4, GHR, C7, ID4, CNTNAP2, NID1, NAV2, CP \n",
      "\n",
      "19:37:18 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:37:18 Read 7809 rows and found 30 numeric columns\n",
      "\n",
      "19:37:18 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:37:18 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:37:19 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a2c0974ed\n",
      "\n",
      "19:37:19 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:37:21 Annoy recall = 100%\n",
      "\n",
      "19:37:22 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:37:24 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:37:24 Commencing optimization for 500 epochs, with 320732 positive edges\n",
      "\n",
      "19:37:24 Using rng type: pcg\n",
      "\n",
      "19:37:34 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:37:34 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:37:34 Read 7809 rows and found 50 numeric columns\n",
      "\n",
      "19:37:34 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:37:34 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
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      "*\n",
      "|\n",
      "\n",
      "19:37:35 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a207f529f\n",
      "\n",
      "19:37:35 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:37:36 Annoy recall = 100%\n",
      "\n",
      "19:37:38 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:37:40 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:37:40 Commencing optimization for 500 epochs, with 322064 positive edges\n",
      "\n",
      "19:37:40 Using rng type: pcg\n",
      "\n",
      "19:37:49 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 7809\n",
      "Number of edges: 261977\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9097\n",
      "Number of communities: 32\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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mCH8vTV6oRXsTRPAABgG8EO5ekK3CSb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\nkknorfvvh0MAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 2603 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  ADAMTS9-AS2, SPARCL1, TIMP3, PTPRM, SELENOP, IGFBP5, GHR, MAGI2, FBXL7, TFPI \n",
      "\t   PTPN13, NFIA, MAGI1, CXCL12, NNMT, SPARC, MGP, KAZN, SATB2, BMP5 \n",
      "\t   EBF3, NRP1, CDH11, DLC1, LTBP2, LEPR, MIR99AHG, FN1, DCN, UNC5C \n",
      "Negative:  MCTP2, PIK3R5, PLEK, LYZ, CD44, NFKB1, BCL2A1, S100A12, ACSL1, TYROBP \n",
      "\t   ATP8B4, JARID2, PLAC8, CXCL8, SLC12A6, FCN1, LTF, C5AR1, IGHG1, AOAH \n",
      "\t   IGHA1, ABCA13, TMEM156, SLC11A1, JCHAIN, PLAUR, OLR1, LCN2, SIPA1L1, IGHGP \n",
      "PC_ 2 \n",
      "Positive:  PCLAF, STMN1, TYMS, HMGA1, PRSS57, CDK6, HIST1H1B, CENPW, SNRPD1, GNA15 \n",
      "\t   AC084033.3, DUT, TUBB, MAD2L1, HIST1H3B, HMGB3, C1QBP, PCNA, LDHB, CENPF \n",
      "\t   MCM3, HELLS, MCM7, SMC4, HMGB1, CENPU, PAICS, RANBP1, HIST1H1D, BIRC5 \n",
      "Negative:  IGHA1, IGHG1, IGLC2, IGHG3, S100A12, IGHGP, IGLC3, JCHAIN, TAGLN, IGHG2 \n",
      "\t   MYL9, CRISPLD2, LTF, TPM2, RRAD, ACTA2, PLN, RCAN2, IGFBP5, RERGL \n",
      "\t   IGHG4, RGS5, SLIT3, MT1M, PELI1, LMOD1, KCNAB1, C11orf96, CRYAB, MMP8 \n",
      "PC_ 3 \n",
      "Positive:  MYH11, KCNAB1, RERGL, RGS5, ACTA2, RCAN2, C11orf96, LMOD1, PLN, RGS6 \n",
      "\t   CACNA1C, PDE3A, NDUFA4L2, CCDC3, PRKG1, SNCG, MYL9, TPM2, RGS16, ERBB4 \n",
      "\t   NR2F2-AS1, TAGLN, MYOCD, SORBS2, CTNNA3, MT1A, MT1M, SDK1, ZBTB7C, RBPMS \n",
      "Negative:  CDH11, DCN, CFH, CFD, CHL1, BICC1, LUM, LEPR, FLRT2, LIMCH1 \n",
      "\t   WIF1, ITGBL1, FMO2, RUNX2, ABCA6, NCAM1, COL8A1, NRP2, FBLN1, TFPI \n",
      "\t   IGFBP3, GJA1, APOE, BMPER, OLFML3, ADGRB3, NHSL1, KAZN, VCAM1, EMP1 \n",
      "PC_ 4 \n",
      "Positive:  LDB2, EMCN, ADGRL4, VWF, PALMD, PTPRB, BTNL9, FLT1, TEK, CDH5 \n",
      "\t   PODXL, TM4SF1, IFI27, CLEC14A, CALCRL, PCAT19, RAMP2, CAVIN2, MECOM, ANO2 \n",
      "\t   EFNB2, PLAT, CLDN5, SOX18, DNASE1L3, GALNT15, FGD5, PLVAP, KDR, NOSTRIN \n",
      "Negative:  DCN, CFD, LUM, CDH11, ITGBL1, ZFHX4, SATB2, COL1A2, KAZN, APOE \n",
      "\t   CHL1, WIF1, BMP5, BICC1, LTBP2, RUNX2, OLFML3, TNC, PTPN13, FBLN1 \n",
      "\t   NCAM1, PDZRN4, FGF7, FAT3, CA2, COL12A1, CLEC11A, ESM1, PTPRD, SLIT2 \n",
      "PC_ 5 \n",
      "Positive:  NLRP3, IL1B, CXCL8, KYNU, PLAUR, EPB41L3, MPEG1, CD14, EMILIN2, RBM47 \n",
      "\t   ITGAX, CD68, PID1, CD83, SIPA1L1, ABCA1, CCL3, IER3, MS4A6A, CXCL2 \n",
      "\t   TLR2, ATP13A3, CD163, CD86, ATP2B1, SLC43A2, SLC8A1, MS4A7, CCL3L1, C5AR1 \n",
      "Negative:  EMCN, RAMP2, VWF, PALMD, PRDX2, PTPRB, ADGRL4, PODXL, ITM2A, BTNL9 \n",
      "\t   TEK, TMEM156, CLEC14A, LDB2, MECOM, IFI27, CAVIN2, CDH5, GIMAP7, CLDN5 \n",
      "\t   SOX18, JCHAIN, IGHG1, TM4SF1, ANO2, SLC9A3R2, AHSP, CHRM3, KLF1, AKR1C3 \n",
      "\n",
      "19:40:47 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:40:47 Read 6474 rows and found 30 numeric columns\n",
      "\n",
      "19:40:47 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:40:47 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
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      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:40:48 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a68d21b6c\n",
      "\n",
      "19:40:48 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:40:50 Annoy recall = 100%\n",
      "\n",
      "19:40:51 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:40:52 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:40:53 Commencing optimization for 500 epochs, with 268756 positive edges\n",
      "\n",
      "19:40:53 Using rng type: pcg\n",
      "\n",
      "19:41:00 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:41:00 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:41:00 Read 6474 rows and found 50 numeric columns\n",
      "\n",
      "19:41:00 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:41:00 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:41:01 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a2b585e93\n",
      "\n",
      "19:41:01 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:41:02 Annoy recall = 100%\n",
      "\n",
      "19:41:03 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:41:05 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:41:05 Commencing optimization for 500 epochs, with 266028 positive edges\n",
      "\n",
      "19:41:05 Using rng type: pcg\n",
      "\n",
      "19:41:13 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 6474\n",
      "Number of edges: 231005\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.8947\n",
      "Number of communities: 22\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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soFu9B8KX91cWnZ4wP/HQh2iBbL9WSTcSfC8gmga3XbyalqVh34m3x7qxjX7BLt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srXknNVkAUUJgByGEeGhH+aEd5bBPETnjhb8BJHgAABBzSURBVNw35hddz6P/MeEW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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 2158 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  CALD1, DLC1, RBMS3, PTPRM, ADAMTS9-AS2, GHR, NNMT, LIFR, KAZN, SELENOP \n",
      "\t   FBXL7, IGFBP5, COL6A2, EBF3, FBN1, SPARCL1, PPARG, CDH11, FRMD4A, ENAH \n",
      "\t   MAGI2, ANGPTL4, NRXN3, NFIB, PALLD, BMP5, IGFBP7, OSMR, LAMA4, NRP1 \n",
      "Negative:  MCTP2, CD44, LYZ, MZB1, CXCL8, HMGB2, PLAC8, G0S2, SIPA1L1, ITGA4 \n",
      "\t   TBXAS1, HIST1H1D, JARID2, FCN1, ATP8B4, DERL3, SLC11A1, PCLAF, KLHL6, RBM47 \n",
      "\t   LTB, OLR1, CSTA, IGHG1, ITGAX, CD69, KCNQ5, AOAH, SLC43A2, IGHG4 \n",
      "PC_ 2 \n",
      "Positive:  LDB2, ADGRL4, PCAT19, VWF, TM4SF1, FLT1, EMCN, PTPRB, ANO2, NOSTRIN \n",
      "\t   EFNB2, CALCRL, PLVAP, CCL14, PALMD, FABP4, IL33, TEK, ARHGAP29, IFI27 \n",
      "\t   CLDN5, DNASE1L3, MMRN1, SOX18, RAMP3, TNFRSF10D, MAGI1, ZNF521, AQP1, GALNT18 \n",
      "Negative:  CHL1, TMEM108, BICC1, DCN, ESM1, APOE, EYA1, CP, ABCA8, BMP5 \n",
      "\t   PAPPA, NCAM2, LTBP2, KAZN, PID1, HNMT, CDH11, CXCL12, CFD, LUM \n",
      "\t   TMEM132C, NAV2, COL1A2, PCOLCE, TENM4, FGF7, FMO3, ABCA6, CCBE1, OLFML3 \n",
      "PC_ 3 \n",
      "Positive:  PLCB1, STMN1, HLA-DRB1, SOX4, TMPO, HLA-DRA, CERS6, PREX2, LRMDA, SPRY1 \n",
      "\t   NAV3, PCLAF, TYMS, HLA-DPA1, FLNB, CENPF, HIST1H1D, NUSAP1, LEPR, CHST11 \n",
      "\t   ATAD2, TOP2A, HELLS, MKI67, CD109, VCAM1, TUBB, BICC1, AC084033.3, P3H2 \n",
      "Negative:  MYH11, KCNAB1, RERGL, ACTA2, SOD3, RCAN2, CASQ2, RGS5, PHLDA2, LMOD1 \n",
      "\t   NDUFA4L2, MCAM, TBX2, PLN, LDB3, MYL9, CCDC3, PDE3A, EDNRA, RGS6 \n",
      "\t   GJA4, LBH, TPM2, MT1A, PTN, COX4I2, CNN1, NTRK3, MT1M, SLIT3 \n",
      "PC_ 4 \n",
      "Positive:  ICAM1, PELI1, G0S2, RBP7, IGHG1, ADGRL4, ST6GALNAC3, IGHG3, IGLC2, PCAT19 \n",
      "\t   TM4SF1, IGHG4, TSHZ2, CXCL8, DERL3, IGHGP, DUSP5, PTPRB, CCL14, PALMD \n",
      "\t   TBC1D9, VWF, IFI27, PLVAP, LDB2, IL33, SLAMF7, DNASE1L3, CLDN5, RAMP2 \n",
      "Negative:  STMN1, HIST1H4C, TYMS, PCLAF, TUBA1B, TUBB, TMPO, DUT, NUSAP1, CENPF \n",
      "\t   TOP2A, MKI67, SMC4, HIST1H1D, HIST1H1B, CKS2, DIAPH3, AC084033.3, KIF20B, HELLS \n",
      "\t   HIST1H3B, TPX2, PTTG1, CLSPN, ATP1B3, ATAD2, ASPM, BIRC5, PCNA, HIST1H2AG \n",
      "PC_ 5 \n",
      "Positive:  WIF1, IBSP, NCAM1, MMP16, SPP1, WDR72, OMD, CHAD, SFRP4, BGLAP \n",
      "\t   SRGAP3, SLC44A5, CADM1, INSC, PTPRD, LMO7, PCDH9, COL13A1, SLIT2, IFITM5 \n",
      "\t   DPT, PAMR1, CDH15, ENPP1, PTH1R, ANO5, S100A13, COLEC12, PDGFD, PTPRZ1 \n",
      "Negative:  CP, ANGPT1, NRXN3, TMEM132C, NAV2, CXCL12, VCAN, ADAM28, PAPPA, COL14A1 \n",
      "\t   ST5, NCAM2, VGLL3, CHRDL1, TRABD2B, CHL1, CCBE1, PDE1A, VEGFC, CLSTN2 \n",
      "\t   EYA1, NTRK2, SYNPO2, VCAM1, PLPP3, LEPR, MSC-AS1, PPARG, LINC00640, FRZB \n",
      "\n",
      "19:43:47 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:43:47 Read 7554 rows and found 30 numeric columns\n",
      "\n",
      "19:43:47 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:43:47 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:43:48 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a34031ff1\n",
      "\n",
      "19:43:48 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:43:49 Annoy recall = 100%\n",
      "\n",
      "19:43:50 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:43:52 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:43:52 Commencing optimization for 500 epochs, with 315142 positive edges\n",
      "\n",
      "19:43:52 Using rng type: pcg\n",
      "\n",
      "19:44:01 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:44:01 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:44:01 Read 7554 rows and found 50 numeric columns\n",
      "\n",
      "19:44:01 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:44:01 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:44:01 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a44bf135c\n",
      "\n",
      "19:44:01 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:44:03 Annoy recall = 100%\n",
      "\n",
      "19:44:04 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:44:05 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:44:06 Commencing optimization for 500 epochs, with 311954 positive edges\n",
      "\n",
      "19:44:06 Using rng type: pcg\n",
      "\n",
      "19:44:14 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 7554\n",
      "Number of edges: 276711\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9071\n",
      "Number of communities: 26\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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EP4wXqbJ1xXqNjBCAQ7AIAh1rcrVhau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\ne53whPzgs7Y/2gYJIYEot3t08qZPW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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 2518 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  KAZN, FBN1, CDH11, OSMR, TMEM108, NAV2, BICC1, PDE7B, COL1A2, SNED1 \n",
      "\t   LEPR, NCAM2, RBMS3, COL3A1, MAGI2, COL14A1, SLC1A3, EYA1, FRMD6, GLIS3 \n",
      "\t   CYP1B1, BMPER, AGAP1, CXCL12, LIFR, COL5A2, SATB2, COL6A3, CHSY3, MGP \n",
      "Negative:  RHOH, IQGAP2, MZB1, BCL11A, P2RY8, LCP1, HLA-DPA1, GNG7, HMGA1, AIF1 \n",
      "\t   SEC11C, CD69, STMN1, PLAC8, HIST1H1D, AREG, BIRC3, HLA-DQB1, HLA-DPB1, KCNQ5 \n",
      "\t   FAM30A, AFF3, DERL3, IGLL1, GYPC, TMEM156, ALCAM, GNA15, FLT3, RAB11FIP1 \n",
      "PC_ 2 \n",
      "Positive:  CFD, COL1A2, LUM, BICC1, LBP, SLC1A3, NCAM2, COL3A1, TMEM108, FBLN1 \n",
      "\t   TNFAIP6, COL1A1, COL6A3, FGF7, CDH11, EYA1, RUNX2, COL14A1, CXCL12, SPON1 \n",
      "\t   ADCY2, BMPER, EFEMP1, SFRP1, ROR2, BMP5, FRMD6, PDZRN4, PAPPA, CNTNAP2 \n",
      "Negative:  ADGRL4, EMCN, LDB2, VWF, PCDH17, CALCRL, RAMP3, PTPRB, TM4SF1, TM4SF18 \n",
      "\t   AQP1, PCAT19, ANO2, TEK, A2M, FLT1, CXorf36, ACKR1, CYYR1, NOSTRIN \n",
      "\t   NPDC1, DNASE1L3, RAMP2, NR5A2, SLCO2A1, ARHGAP29, PALMD, ECSCR, CLEC1A, RAPGEF4 \n",
      "PC_ 3 \n",
      "Positive:  STMN1, CHST11, HMGA1, HLA-DRB1, TYMS, PCLAF, AIF1, PRSS57, DIAPH3, NUSAP1 \n",
      "\t   CENPK, ATAD2, GNA15, HELLS, DTL, CENPF, AC016205.1, MKI67, IQGAP2, CLSPN \n",
      "\t   RNF220, TOP2A, SMC4, CHEK1, SMC2, ATP2C1, HMGB2, ATAD5, CENPW, SMIM24 \n",
      "Negative:  RGS5, SOD3, PHLDA2, MYH11, MRVI1, RCAN2, NDUFA4L2, RERGL, MCAM, CASQ2 \n",
      "\t   DGKB, LDB3, TBX2, PDGFA, TINAGL1, ACTA2, PDE3A, KCNAB1, CDH6, SYNM \n",
      "\t   CNN1, MYL9, ERBB4, CTNNA3, ADIRF, PTP4A3, GJA4, LBH, MT1A, NMNAT2 \n",
      "PC_ 4 \n",
      "Positive:  DERL3, FKBP11, SLAMF7, MZB1, SEC11C, TNFRSF17, CD27, IGHG1, JSRP1, PRDM1 \n",
      "\t   TBC1D9, IGHA1, DCC, SCNN1B, TSHZ2, TMEM156, FAAH2, PAIP2B, DUSP5, IGLC2 \n",
      "\t   RASGRP3, PELI1, AC092546.1, IGHA2, IFNG-AS1, ZNF215, ZBP1, IGLC3, SLAMF1, DNAAF1 \n",
      "Negative:  PRKG1, MYH11, RGS5, SOD3, MRVI1, RCAN2, PDE3A, RERGL, INPP4B, CASQ2 \n",
      "\t   NDUFA4L2, DGKB, PHLDA2, MCAM, LBH, LDB3, TBX2, ACTA2, CACNB2, STMN1 \n",
      "\t   ATP1B3, CDH6, KCNAB1, TUBA1B, TINAGL1, PDGFA, ERBB4, TYMS, CNN1, ZFHX3 \n",
      "PC_ 5 \n",
      "Positive:  S100A9, S100A8, MNDA, S100A12, BCL2A1, CSF3R, LCP1, CXCL8, PIK3R5, PLAUR \n",
      "\t   C5AR1, LYZ, ACSL1, SLC11A1, TYROBP, MMP9, AQP9, G0S2, DOCK5, FCN1 \n",
      "\t   FCER1G, HCK, ITGAX, RETN, LST1, SIPA1L1, MXD1, CRISP3, AL034397.3, TREM1 \n",
      "Negative:  MZB1, SEC11C, FKBP11, DERL3, RAB30, SLAMF7, TMEM156, GNG7, EIF2AK3, HSP90B1 \n",
      "\t   JSRP1, TNFRSF17, PRDM1, DCC, CYTOR, AC092546.1, ZNF215, FAAH2, TBC1D9, CD27 \n",
      "\t   PAIP2B, GPR176, CARMIL1, TTLL7, CEP128, SCNN1B, IFNG-AS1, RASGRP3, TEX14, DUSP5 \n",
      "\n",
      "19:46:58 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:46:58 Read 10046 rows and found 30 numeric columns\n",
      "\n",
      "19:46:58 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:46:58 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:46:59 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a20f96dbd\n",
      "\n",
      "19:46:59 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:47:02 Annoy recall = 100%\n",
      "\n",
      "19:47:02 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:47:04 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:47:04 Commencing optimization for 200 epochs, with 411964 positive edges\n",
      "\n",
      "19:47:04 Using rng type: pcg\n",
      "\n",
      "19:47:09 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:47:09 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:47:09 Read 10046 rows and found 50 numeric columns\n",
      "\n",
      "19:47:09 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:47:09 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:47:10 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a13a5f5ac\n",
      "\n",
      "19:47:10 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:47:12 Annoy recall = 100%\n",
      "\n",
      "19:47:13 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:47:15 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:47:15 Commencing optimization for 200 epochs, with 414526 positive edges\n",
      "\n",
      "19:47:15 Using rng type: pcg\n",
      "\n",
      "19:47:20 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 10046\n",
      "Number of edges: 349048\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9113\n",
      "Number of communities: 28\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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EcrFgAAjIVgl6xOK3a4BcXSuWNHxQ4A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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 3349 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  KAZN, CALD1, CDH11, RBMS3, NAV2, CHL1, DLC1, NNMT, LEPR, TMEM108 \n",
      "\t   PTPRM, FBN1, FBXL7, CXCL12, EPAS1, GHR, BICC1, ERRFI1, FSTL1, EBF3 \n",
      "\t   EYA1, LIFR, IGFBP5, ANGPTL4, BMP5, NCAM2, LHFPL6, ENAH, VCAN, FRMD6 \n",
      "Negative:  HIST1H1D, MZB1, STMN1, AREG, KLHL6, CD69, HLA-DRB1, HLA-DPA1, KCNQ5, HLA-DPB1 \n",
      "\t   BIRC3, HMGB2, HLA-DQB1, TBXAS1, IGLL1, S100A4, AFF3, FLT3, RGS1, CD44 \n",
      "\t   DERL3, TMEM156, PCLAF, EIF2AK3, MED12L, UBASH3B, TYMS, PLEK, PMAIP1, TFRC \n",
      "PC_ 2 \n",
      "Positive:  CHL1, CP, NCAM2, EYA1, LEPR, VCAN, NAV2, ADAM28, PAPPA, CXCL12 \n",
      "\t   TMEM108, ESM1, CHRDL1, TMEM132C, CCBE1, PID1, PTGDS, FMO2, BMP5, LPL \n",
      "\t   ROR2, TRABD2B, LINC01268, BMPER, NLGN1, TF, LINC02384, ABCA8, SLC1A3, VCAM1 \n",
      "Negative:  TINAGL1, MCAM, MYH11, RGS5, CASQ2, NDUFA4L2, RERGL, SOD3, MRVI1, CCDC3 \n",
      "\t   PHLDA2, DGKB, LDB3, ADIRF, NTN4, ACTA2, RCAN2, PRKG1, PDGFA, COX4I2 \n",
      "\t   LMOD1, GJA4, MT1A, CACNA1C, KCNAB1, MYOCD, ENTPD3, SNCG, CAV1, PDE3A \n",
      "PC_ 3 \n",
      "Positive:  DERL3, MZB1, CARMIL1, TNFRSF17, IFNG-AS1, JSRP1, IGHG3, COBLL1, TBC1D9, IGHG4 \n",
      "\t   IGHG1, DNAAF1, SLAMF1, IGHGP, DUSP5, IGF1, IGLV3-1, AC007569.1, BFSP2, BIRC3 \n",
      "\t   AIM2, OTUD1, FAAH2, AC092546.1, IGHG2, CYTOR, EIF2AK3, TXLNB, AC012368.1, TMEM156 \n",
      "Negative:  TUBB, STMN1, TYMS, PCLAF, CENPF, TUBA1B, DUT, DIAPH3, MKI67, NUSAP1 \n",
      "\t   TOP2A, HMGB2, HIST1H4C, PCNA, AC016205.1, ASPM, KIF11, SMC4, KIF20B, DTL \n",
      "\t   CENPP, ATAD2, TPX2, HELLS, BRIP1, HIST1H1B, CLSPN, CDK1, NCAPG, KIF15 \n",
      "PC_ 4 \n",
      "Positive:  ADGRL4, PTPRB, MCTP1, PCDH17, CD93, THBD, FLT1, CALCRL, PCAT19, EMCN \n",
      "\t   RBP7, TM4SF1, DOCK4, TEK, LDB2, PALMD, RAMP3, CNTNAP3B, DNASE1L3, PLVAP \n",
      "\t   BTNL9, IFI27, NOSTRIN, CXCL8, CLEC1A, SIPA1L1, PLAUR, KDR, ADGRF5, TM4SF18 \n",
      "Negative:  PRDX2, GLRX5, AHSP, HBB, CA1, ATP5IF1, HIST1H4C, PCLAF, HIST1H1B, HBD \n",
      "\t   HBA1, BLVRB, KLF1, TMEM14C, FAM178B, TYMS, HIST1H1D, HIST1H3B, NDUFA4L2, MZB1 \n",
      "\t   SOD3, CASQ2, MYH11, ACTA2, RERGL, TAGLN, UROD, HIST1H2AG, RGS5, DUT \n",
      "PC_ 5 \n",
      "Positive:  ACSL1, S100A9, S100A8, MNDA, PIK3R5, PLAUR, C5AR1, LYZ, SLC11A1, TLR2 \n",
      "\t   CXCL8, BCL2A1, TYROBP, SIPA1L1, RNF144B, ITGAX, IL1R2, PLEK, AQP9, GK \n",
      "\t   RAB31, FCER1G, FOLR3, S100A12, AC020656.1, NLRP3, G0S2, CSF2RA, FRY, FCN1 \n",
      "Negative:  NRP2, ADGRL4, WIF1, CADM1, PTPRB, MMP16, MYRIP, BGLAP, IBSP, NCAM1 \n",
      "\t   PCAT19, WDR72, CNKSR3, EMP1, TEK, PCDH17, EMCN, SFRP4, SPP1, TM4SF1 \n",
      "\t   INSC, GNA14, SLC44A5, PALMD, S100A13, PTPRD, LDB2, RAMP3, SRGAP3, NOSTRIN \n",
      "\n",
      "19:51:25 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:51:25 Read 7813 rows and found 30 numeric columns\n",
      "\n",
      "19:51:25 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:51:25 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:51:26 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a61e2d173\n",
      "\n",
      "19:51:26 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:51:28 Annoy recall = 100%\n",
      "\n",
      "19:51:29 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:51:30 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:51:31 Commencing optimization for 500 epochs, with 323000 positive edges\n",
      "\n",
      "19:51:31 Using rng type: pcg\n",
      "\n",
      "19:51:39 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:51:39 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:51:39 Read 7813 rows and found 50 numeric columns\n",
      "\n",
      "19:51:39 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:51:39 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:51:40 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07aa59268\n",
      "\n",
      "19:51:40 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:51:42 Annoy recall = 100%\n",
      "\n",
      "19:51:42 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:51:44 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:51:44 Commencing optimization for 500 epochs, with 321014 positive edges\n",
      "\n",
      "19:51:44 Using rng type: pcg\n",
      "\n",
      "19:51:53 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 7813\n",
      "Number of edges: 274035\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9170\n",
      "Number of communities: 23\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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wGUERu7aYmdE2nFHjsAjfE8sNtSitVh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VE8nUjkB4x3hodzDSFp56iCwy9WvneNhl\nNH6pofIz1aUZqZ9et6o6mEj9ZThwb7vv1pb6lT8hqI2c0VBqpBghxMZxSUHkJUknE3rUHtgl\nBJRi1W4ylX5/IhJJpWudtvX5ebr4vVHUdJrfGQxvLHBjWxJkltdsurCy+MLK4pQofjAReco3\n/H4osvDHvrmu4azi/Ey9KEPIbesawqn0i4OjXovpmobKTD0zqITip2IP9Sh2GdUe82SE2v+S\ncsZOA3svrBptd/LKcOC+Dt+hfanr813fO3ENAo6sen00yIsS6rCQPSaD4dRCTyTNLxrY2bgM\n32lzDPPdE1ff+k7bI10DBWbThZXFmX1+UNZsYKdkxk5fgZ3aM2FxQSunYrW4x65/Jv6Tfd2H\nnzbaG5q6u61HwSXpwZaRoMlgOKvYq/RCQONOyHctLF3ZOHZtFoZGWFn2zpObKuzWe9p73hqb\nyPjzg4JUE9jpZZud2gOmlFZKsRaWZRitlWJfGw0KC5qgbg+E0K4vewKJ5N5w5MzifAW/JUEn\niizmG1ZXHx7ZsQxzc1NdlvpW5JmMPzq5yWXkfrDn4P7J6Wy8BChC8cDObuTIXNcVPVB7wJQQ\nNZKxYwixsqzGSrGR1CIHyAVJmk7p5cYo9171ByWJbCotVHohoAuX1Zb/tHX9RZUlpxZ6PlNd\n+tBZGz5eXpS9lyu3WX50chNDmH/evX/RqWhAI8VPxept8pPa682a2WNH5MBOW6msEqt54YMW\nlvWYsccuW7aOjDuN3GmFHqUXAnqx1u3MRu31SFbnOb574urv7N5/x66O+1vX5ZsxXJt6cUEw\nswYFT6TKhyeQsVMLzZyKJYRYOTbOi0qvIpM+WlZoX3ATdmFlsZH+voPqNBCNd01FP1Li5Qw4\nfAyadVqh5471jaPxxO272rN3MRYlKZRMzd9KAlkQ5XkFx06Qv7Ub00spSe0Zu4RW+tgRQmwc\nq7EaZb7ZVO+07w1Pyf/IEIYwZFMJTmtmi9wq9nzUYUHrPlpWOBpPPHxw4F/f2//jk5sye684\nleYf6ux7eXg8LYo2jv1cddnVDRUm3I5mTYwXFqYAcklvpVi1f5TnSrFqX+dy2DRXit0yMr43\nPHVBedGjHznpvtPX3Xv6OpaQe/f7xAUnKmCF0qI4GI1v9Qe9ZtP6fJfSywHIuqvrKz9bXfrB\nROTHe7sz+I0iSeTb73b8cXAsLYqEkBgv/KZn8B6c5c+mGC8oe9jLxnFET4Gd+jN2AtHESDFC\niI3T1OGJSCr9wP5ej9n4D2trnUauwm4lhGyuLX/CN/T7fv/na8qUXqBGxHjh5519fxwckw8g\nVztsU2kenQJBD/5xbe14IrXVP15sNX95dXVGnvO90OTCI7cvDwduWF3txX6+7IjxQoFFyfcW\ne+zURUuHJ2wcmxZFXtRINut/9vdOptJfW1vnPKzl45caKkttll92DYwnUgquTUt+sOfgCwOj\nh9rK9M/E/mX3fqREQQ8MDPMvJ6xq8bjk28WMPOfeiamFD0qE9M/EMvL8sFBcEBQcO0HmSrH6\nGRer9sAuIYgsw2hjq7hVQz2Kd46HXxkZP6Mof964UjNruKWpLsYL93f4lFqblgxG49sDoXkP\n7p+c3hde5OIEoD1m1vCDjWurHdaf7ve9PnqcjYsHovE/DI7+YM/BzVt3/bpncNGfsXNqr19R\nipekpCDaFH17rRzLMCjFqkZSEDTQxE42dzCH+qkmcUG4p73HzrFfb15kYvephZ7zSgte9Qff\nHgudmbmBkvp0pFZeg9E4dtqBTjiN3J0nN9+0fe+P9hz0mJprnbaBmbjLxJXbLIYjd9Don4nt\nCU3tCUX2hKZCyRQhhGFIrcN+WqH7Ff94WvhQ0rvYam502bP+N9GluNLdiQkhDCE2ltVPKVbt\nEUZCELVRhyUaOpjzi87+sXjyG831R9o28Y9ra3cFJ+/v8J1UkKfsKXfaOY5wD+Ayqf03FyCD\niq3mO09p+vqOfd/a1S6KkkBm95ve1lLf4pm9w5EI6ZuOyZHcnlBkMpUmhDAMqXfazystOCHf\ntT4/T76pPiE/7+72Hnmfj+ySqpIlYkRYCcXHTsjsHKeBi+8yqf3ykBBEbRyJJXOl2ATlpdj9\nk9PPDYyuz3ddVFVypJ/JN5tuWFX93+09v+oa+Oqa2lwuT2PWup1FFnMgkTz8QaeROzE/T6kl\nASii3mk/pdDzmj946JH+mdgduzp+ckpz70x0dzDyQSgiz8IxMEyl3XpWsXdjQd6JXvfCCsnH\nyotOyM97dTQYiCc9ZtPTvcO/7R35ZEUxziRlQ1QdgZ1VW4cXl6b2wC4laqcUK+euqM4G86J0\n175ujmFua2lY+vb24sqSl4cDz/T5P1pW2Ohy5Gh9msMxzA2rq3+45+ChR5xG7tsnrDpSJg9A\nqwRJ2jkenvdgXBC+tmMvIYRlmEaX/YLyIjkzd9SuaUVW8+W15fL/L7aYfrS366cdvu9sWJ2N\nleucGkqxhBA7x45/+A5Zw9R+eYgLomaOoGugFPtYz2DfTOz6VdWVduvSP8kw5NaWhhvf/uDu\ntp4HWtejzHF8JEJeGhpjGHJ1fZUkScVW89klXtr3aAIch6kUv+iXZ73T/uXV1S0e13GHDh8r\nL3pzLLTVHzy7xHsO+qtnmnxeUA2BXR/NF99jovZkWFIQNZOxkz/ZcWo/W30zscd8Q/VO++V1\n5cv5+TqnbXNteWdk5rmB0WyvTau2jIx/MBG5qLLk7xorr1tVdWFlMaI60CeHkV20PcLpRZ5T\nCz0rjBtubal3m4z/3e4LJ9MreR5YaHaPndKbrW0cFxcEnTSKUnvMlBAEzey+nw3s6NxjJ0nk\n7rYeUSLfXNew/FnOclu7hw/2o63dcYjxwv8e6HMZuetXZaY1KwC9jAbDR4rnp9MMDHNeaQZy\nbG6T8baWhkgq/Z9t3St/NjicPKFV8YydjWMlidbr77FSdWAnEZLSUMbOylJciv39gL8tPHVp\nTdnqvGPYMGdmDV9vro/xwk/R1u7Y/d/B/olk6sa1tcjSARBCbmmuO8nrPvSPNo79ZktDnTMz\nbUrOLM4/v6xweyD08nAgI08IMpWcirXpafiEqi8YSUGQCLFoYp4YoXmPXSCefPhgf4nVfG1j\n5bH+2VMK3OeWFryGtnbHyDcd/cPA6DqP64LyIqXXAqAKTiP3n6c27wtP9UxFXSbjhnxXfkZ3\nYN/SVLc3FLm/w7chP6/Ias7gM+uZSgI7++z1lydEI7v2l6DqmCkhiIQQM0qxSru3wxfnhdta\nGo6vp+BNa2sdRu7+Dh+Nf3dFyIVvQsjNzXU4dQJwuHUe12eqSzeVFmQ2qiOEOIzct9Y1xnjh\nrn3d+tiLlQtzgZ3CWSRdZexUHdglZwM7VS9y+Sgtxb4yMr49EPpERfHGAvfRf3ox+WbT9auq\nAonkr7oGMrs2rXpxaLRjcvrzNWX1GSozAcBynFzg/mRF8e6JyRcHceQrM1RyKpbeitlxUHXM\nJGfsNNOg2MwaWIah64MVSaV/tr8332y6cU3NSp7nksrSZo/zmT5/19RMhpamWVNp/uGDA16z\n6UsNx1z4BoAV+oe1tcVW88/29w3HEkqvRQvUcyqWIGOnBklBIBrK2BFCrBxLV7uTn+7vnUyl\nv9ZU51zZ/n2GId9objAQ+WgtqhxLefBAbySVvqmpVvF7XAAdsnHs7esak4Lw471d+LJauRgv\nGA2GRVvV5NJhe+y0T9UxU1KUM3baubzZWDZGzz6znePhLSPjrUX555R4V/5sdU7bpbVlnZGZ\n59HW7sjawlN/GQpsLHCjUSqAUk705n26urQtPPVsv1/ptVAvxgtHHQSSAyjFqkVCW3vsCFXj\n6hKCcG+7z8axX2+uz9RzXtNQVWqz/AJt7Y5AkKT7OnycwXBzU53SawHQtb9fXVNhtz7U2d83\nE1N6LXSL8YJVBYGdHYGdSiQEgWhojx0hxMoaaCnF/ryz3x9P3LimptCSsaNnZtZwS1NdjBce\n2I+2dot4ts/fPRX9Ql35USe2AUBWmVnDP61vFCTpzr1dPAqyKxAXVJKx4wgCO8XtnpiU0+Cv\n+0OaSfDIU02UXsXR7Z+cfmFg9IT8vAsrSzL7zKcWes4tLXhjdGJbIJTZZ6ZdKJl6tHugzGa5\nsr5C6bUAAGlyOzfXlB2MzDzlG1Z6LRSL8rwaxkchY6e8B/b3fmtn+56JCCHkzbHgNW+8997E\npNKLygAbDaVYXpTu2tfNMszXm+uzsd9Vbmt3H9rafdgD+3ujvPCPa2tNWunIDUC761ZV1Tpt\nj3QP+KajSq+FVjFeUPwc2GQq/Uj3ICHkNX/w/g7fZErjE4HVeAk5EJl5pm/k8EcSgnB3W48G\nsuFWjhUkKSWKSi9kKb/uGeybiV3bWFntyEpBcLatXTz5SNdgNp6fRrsnJl/1B88u9rYWYTgH\ngFoYDYZ/Wr+KSORHe7p4UQOXoFwTJSkliMqWYgOJ5A1vffBs3wghZJrnf9/vv+7N98fiSQWX\nlG1qDOzeDS6SnBuJJUbo7ypkU32P4oFo/EnfcJ3Tvrm2PHuvckllaZPb+bu+EbS1I4TwonR/\nu8/MGr66tlbptQDAhzS67F+or+iZjv66BzeixyzGCxIhyh6eeLxnKJT80G6uyVT6N5r+r6nG\nwC59hITWkR6niMpPXIuSJLduun1dA8dkse0Qw5BbW+oZtLUjhBDyZO/QQDR+TUNVCcZTAqjP\n1Q2VjS774z1DnRHciB4bNYydaAtPL/bgVO5XkjNqDOxWuRwLH7RzbLnNkvvFZJbKA7vf9/v3\nT05vri1blbfIf4LMqnPaL61BWzvijyce6xmqdtgurS1Tei0AsAiOYe5Yv8rAMHfu7VL5Rhq1\nUcPYiSOkKLQ8hVuNgV1rkafF45r34LWNVUb6N5XLGZpak/MAAB3JSURBVGl1HhoYiycfPjhQ\narXkbJLVtY2zbe2CWjn1fBx+2tGbFMSbm+qymiIFgJWoc9quaajsn4k9fBADr4/BbGDHrWhw\n0Qqty58fThBC1i/2oGaoMVQyMMxPTmm6qr6i1GYxs4bVeY7vnbjm8zVayGfINy7qbGV3d1t3\nQhBuXVefs1EfaGv31tjE9kDoY+VFJ3rzlF4LACzlyrqKdR7Xb/uG94a0XMXLrLnATsmM3VX1\nFUWWD+1yyTMZv1iv5UncSsbRS7Cw7PWrqq9fVa30QjLMqtZS7MvDgV3ByQsrizd63bl83VML\nPeeUFLw+GtwWCJ2hswOhSUH82f4+G8d+ZbXWPucA2sMw5JvrGr7y9gc/3tv1f2dvUENvNvVT\nQ2DnNZv+76wNT/YO7wlFkoLYMxUtt1kKMtd4X4VUGthp1eweO3WUYoOJ1IuDo0PRhNPIbfWP\n55tNX1ldk/tlfK2pdvfE5H0dvhO9ebr6rny0e9AfT9zcVOc1a/krBkAzKu3WG1ZVP7C/96HO\nfsz9Ww41BHaEEIeRu2EuT3TXvu4/DY29PxHRcJ0EgV1OqacUuys4+b33Dhy+2+9j5UVOowKf\nh3yz6brGqvs6fD/cc7DKbjMamA3evA35mv2Vkw1F47/rG2l0OS6pyvBsDwDIns9Vl709Fnq+\n399alH9KQU7rGzRSw6nYeb7UUPnXkfGHu/rv965Xei3ZosY9dhqmklKsIEl37euad4Zjy8j4\nsEKdAj9WVmTj2LfHQk/4hh7tHrz1nbYf7DkoKN0GJSmIE8lsneq4p72Hl8RbmusMODMBQA+G\nIbevb7Ry7F37uqbTvNLLUTuVZOwOV2w1f7KiqD08vXM8rPRasgWBXU6ppN2Jbzq28CCqKEnv\nBpX5oD/aMzDvPdkyMv6noTFFFkMIGYzGb9/V/qlXtm/euutzW3Y+1+/PbIy5ZWT8/YnIRRUl\nTW5nRp8YALKuxGq+cU1NMJF68ECf0mtRu7gK2p0sdHV9pclg+GXXgFZ7qCKwyymV7LFLHmEB\nSUGZFk1vj4WW+WAOTKX5W3bsezc4KWcMJ1Pp+zp8T/dmbAp4lBd+dqDPbTLegDMTAHS6sLLk\nlAL3n4bG3hqb8E3HDkZmlPryVDn5jl3ZyRMLFVhMF1eVdEZmtil0lck2BHY5pZI9dtUOG2dY\npALY4LLnfjGEkOhib8gMr0yZ489DYwtHRD/lG85UZfjhg/2hZOora2oU2dEIACvHEPLNdY0W\nlv3e+503vPX+jdv2XLp15+/7/UqvS3ViPE9UVoqVfaGuwsKyv+waUHrLT1YgsMspzsAYDQbF\nS7FOI7e5Zv4o2A3evBNz2+vkkCq7deGDNQ5b7ldCCBmMxhc+OJlKR9Lzo73j0DUVfX5gdJ3H\ndUF50cqfDQCUMhpPJAXh0ETEKC/c3+F7eTig7KrUJiYInIExqW+4gMds/Ex1iW86+vpoUOm1\nZJ7q3m7Ns3Gs4qVYQsh1jVV5JqOBYRiGeM2mK+sqfrBxrVLb+K+sr5j30mbWoFRLavsRmqR/\n7/0DLwyMhlZwnEKSyL3tPQwhtzTX48QEANVeGBhdmOt5Dkm7D4vxgto22B1yeW25jWMf6R7Q\n3rxyFINyzcayipdiCSGvjQYjqfS1jVVX11cofjDz9ELPv29c++CBvqG5bNnnqsuUytidWZy/\ncEedx2TqjMzsDU3d1+Fb53GdU+I9u8R7rP3nXhoa65ic3lxbVudU5q8GAJniX6yHwIhCjQVU\nK8YLKqzDyvJMxs/XlP26e3CLP/ixskKll5NJCOxyzcopX4qVCHnCN2zj2M9Wlyoe1cnOKMo/\noyh/Ks2nRfH6N99/YzR4/aoqRda2zuO6rLb88Niu2mH98cnNThO3PRB+3R/cGQzvCUXu3y9H\neAUfWV6EF0mlH+rsK7CYrmmoyubyASAX3Cbjwgc9aDb+YWoO7Aghm2vKft/vf7RrYFNpAauO\nS2FGILDLNSvHRmJJZdewIxDyTUcvqy1X2/59l5EjhHy2puyRroHXRoObSpW5i5KIRAi5oLww\nz2RqcNnPLS3gGIYQsqm0YFNpQVIQ35uYfG104u2xib2hqZ/u9zW7Xa1FnnNLCkptlnlPNZ3m\ne6aiFo59rt8/lea/27JazV9zALBM55cVbgvMP1MpSmKUF+z4HZ8T44WF34rq4TByl9WWPXxw\n4OXhwCcripVeTsao67queR2T0xOJVCSdftI3fGFlsVJx1RO+YaPBsLlWmU1sR3VpTdnveod/\n3T10Xklh7m+iIqn0iwNjjS777etXLfriZtbQWpTfWpSfFOoPRXht4amHOvurHbZzS7wfKy8q\ns1kkQh7tHnyiZyglzvZBqHXazykpyOXfBQCy5LzSAt909EnfsNxKnSGkzGYZjCa+um3Pv5+0\nttqxyIEwXemaij7pGwon02lJen7Af3FliUqqQ/N8vqbs2T7/o92DHysrWrRZBI0Q2OXOQ539\nT/qG5F2aP+/se6p3+M6Tm1bnOXK8jL2hqbbw1EWVJaodUWrn2IurSp70DW8fD51RlJ/jV3/S\nNxwXhC82VB71V/xQhJcS63cHZyO8R7oHH+kerHbYii2mncHJw3++bybaMTmNpsQA2nD9qupP\nVBTvCUUEUVrrdja47C8Ojt7X7vvH7Xu+vX7VmcW5/u5SjzdGJ77/Qad8KGEqlb633fdOIPyD\nk5tUGDdZWfay2vKfd/a9NDSmmQGPOBWbI/snp5+Yi+pkkVT67rbu3K/kcd+QgWEur5vf7kRV\nLq0pM7OGx3qGcvy6kVT6hYHReqf9zGLv8v+UyWBoLcr/9vrGZzad+v9OWrOptHA8kZwX1RFC\nJIn8cVCxcRoAkHHlNsunKoovriqRm4BeVFnyo1OaWIb57vsH5n3h64ckkQf2++YdNd0xHlbt\nCK/PVpfmm02/6Rk8VF2hHQK7HFn0M901Fc3eNNJFdU9Fd42Hzynxlqt43wMhJN9s+mRF8f7J\n6d0T88OjrPpt30hcEK5eRrpuUWbWcHax9zsbVj2z6dRFs/qjcRyaA9CyjV73g2ecUOOwPtTZ\n/4MPOnU4kWI0kRhfMLKSELIvPJX7xSyHmTVcWVceTKT+MDCq9FoyA4FdjsSP8OudyO2v/RO+\nIULIlXUVuXzR43N5bTnHMLlM2k2n+ef6/bVO20eOJV23KDNrKLSYFz6u2vI3AGRKqc1yf+v6\nc0q8W/3Br+3YOxZX+LQcHNXFVSWFFtPjvqGECrrMrhwCuxypX6x1mcPIFVsXufxniT+WeGN0\n4pRCj1Kjw45JsdX80bLCDyYibbm6z/tt70iMF66ur8zIHt+PLzZbYtEHAUBjrCz7byeu+fLq\n6p7p6N+//cF7ua08KKvEYila7LZ2vceV+8Usk8lguKq+MpxMP9evhaQdArscObe0YGHH3avr\nK7gcHhR63DckSNJV9RSk62RXNVQaGOYJ3/x2wdkQ5YXnBvzVDuu5GTq4elV9xYWVxYf+85pZ\nwz+urT25QJmhbQCQYwwhV9ZV/Ghjk0jIHbs65GqJHjAMuXZVFfnwla21KP+UQo9CK1qWT1UW\nl1otT/qGFG80u3I4FZsjRoPh7tNafnGw//XRiWiat3BsnBdyOVwhlEy9Mjze5HauU/Ft0zzl\nNsvZxd7XR4NdUzONruweH/5d7/BMmr+lqS5TkTbHMLe1NFxWW94ZmTEaDOs8znzUYQF05tRC\nz/+0rv/X9w481NnfOx27raXBzGo/n7IjECISaXE7o4LgMZnOKfFeWFmswiOxh+MY5qqGiv/c\n1/1s/8jV9ZVKL2dFENjljttkvK2l4baWhpQoRlL8tW+8d3+H7xdnn5ibAclP946kRPHqBso+\nr1c3VLwxGnzSN/yvG1Zn71VivPBsv7/cZjm3NMN95irt1kq73jtaAehZhd36QOv6O/d2/XVk\nvH8m/u8nrSnK4Q6c3HtlOPDG6MSm0oLvZPNLOxs+UV70lG/4Kd/wp6tK1da9/5ho/9ZBhUwG\nQ6HF9MWGyuFY4unekRy84nSaf3FwtM5pP03dyfCF6p320wo9r49OHBojmw3P9I1Mp/kvNlRq\naaoMAKiEjWP/30lrvry6unt65sZte/aEIkqvKFsmkqmf7u91m4w3NdUpvZZjZmCYqxsqo7zw\nu75cXJezB4GdYuRh8I/1DPqz3wLj9/3+GC98ob6cxrDl6oZKUZKyt9MuLgjP9vvLbJbztTUH\nGgDUQ95y9x8nrU2J4rd2tWu1peVd+7qn0/ytLfWLztJVv/NLC6od1t/1jkym0kqv5fghsFMM\nyzC3NNenBPGBjt6svlBSEJ/r95faLJTOs2pyOzfk570yHMhS14Bn+/yRVPrqeqTrACC7Wovy\n7zt9faHF/F9t3Xe39fCipnoYvzg4unM8/ImKorNW3DFKKQaGuaahKi4IT/fm4tBeliCwU9I6\nj2tTWeG2QGj7gmHSGfTi4OhkKn1FXTm9gctV9RW8JGXjNy0hCM/0jcitVTL+5AAA89Q5bQ+e\nccLGAveLg6O37mwLJVOEEImQQCJJdRO10XjywQN9BRbTV9fUKr2WFTmntKDBZX+ufzScpDVp\nh8BOYf+wptZh5O7t8GXpV5qXpN/1jXjMxo+XUdxBbWOBu8nt/OPgWMYHdTzXPzqZSl9dX6mZ\n8c8AoHJOI/fjk5uurKtoC0/duG3PA/t7P/3Xd6549d0LX95xx672rO4nzhJJIj/Z2xXnhW+2\nNFB97IAQwhByTUNVQhAep7ZDDQI7hXnMxmsaKgPxZJb2kP11eHwsntxcU077Gfsr6spTovhs\nnz+Dz5kUxKd7h4us5gvQNxgAcsjAMF9eXf2dDavCqfQzfSMzaZ4QIhGyKzj5jXfa5H+kyLP9\nIx+EIpdUlZ5K2/m8RZ1ZnL8mz/GHgdFFZ6OpH90Xe234bHVpg8v+hG9oMNM3apJEnuodtnPs\nxVUlmX3m3Duz2FvntD0/4J/O3Ffe8wP+yVT6qroKpOsAIPc+UlJgZOZfhSeSqb+OjCuynuMz\nEks8fHCg1Gr58upqpdeSMdc2VqVE8bGeQaUXcjwQ2CnPwDC3NNULovTTDl9mn/nNsYn+mdhn\nq0vtHJvZZ849+UxZjBee689M0i4piE/3jhRZzJ+oQLoOABQQSqQW3YQzQE81VpSkH+45mBCF\nfzqh0Ub/heaQUws9LR7XS0NjoxSO+kVgpwrNHucnKop3BSdfH53I4NM+4Rsys4bP1ZRl8DkV\ndF5pQbnN8kzfSDwT+xFfHBwNJVNX1JUbc9IgGgBgHhvHLlosoGib2lO9wx2T05fWlFE002iZ\n/q6xihelX3fTl7TDJU0t/n5NTZ7J+D/7fRmJWgghu4OTnZGZT1UUU9pPaCEDw1xRVzGV5l8c\nWGkLqLQoPtU77DWbPllRnJG1AQAcK4eRO9G7yPzoGicd42r6Z2KPdA1W2a3XNWqnCHvIid68\nDd68vwwHMr5LKtsQ2KmFy8hd11g1nkhl6v7gMd8QxzCX1ZZn5NlU4oKKoiKr+ane4aQgruR5\n/jg4FkykrqyvoP1MCQBQ7baW+qrDpg6yDDEw5L/bfB+ofjqFIEl37u0SJOmfTlil1S/S6xqr\nREn6DW1JO23+x6DURZUla93O3/aO+KZjK3yqA5GZDyYi55cVFmtrKCHHMJtrykLJ1MvDgeN+\nEl6UnuodzjebLkS6DgAUVWqz/N9ZJ/7LCauurKu4cU3NL88+6a5TWkRJun1Xu8qPUPyme7Az\nMnNlXcWaPIfSa8mWFo/rlAL3Fn+wf4ampB0COxVhGHJLU51EyL3tPSvsR/5YzyBDyOV1mkrX\nyS6qLHGbjE/6hgXpON+kl4bGxuLJy2upbwEDABrAGZjzywq/vLr6stryCrv1RG/e/a3r882m\nH+05+EjXgNKrW1zXVPSxnqF6p/1LDZVKryW7rl9VLUnSI90q/Q+xKFzY1GVVnuPCyuJ94amt\nK7hX65+JbwuEziz21jhsGVybSsjHQfzxxPG9RbwkPekbyjMZL6pCug4A1KjGYXugdX2Dy/FI\n9+Bd+7qP+yY2S9Ki+OO9Bwkh31rXoPleUavyHKcX5b/uD/ZMR5Vey3IhsFOdG1ZVu03G/znQ\ne9w9Kh/3DUkSuUKL6TrZ56pLHUZO/mseq78MBUbjySvqyq2sdk7mA4DGeM2m/z695bRCz5+G\nxr79bkeMV9G0sV92DfimY9c0Vq3SbhH2cNevqiYMeaSLmp12COxUx2nkvrK6JpxM/+q4kvCB\nRPLVkfGTvO4mtzPja1MJG8d+uqqkfyb+VuDYusPwkvR4z5DLyF1cSX3HZgDQNivL/sfGtRdX\nlrwbnLx5x76gOqYgdExOP9070uhyaHKrz6LqnLazi71vjU0ciMwovZZlQWCnRhdUFJ2Qn/fc\nwGjX1DHnfp/0DfOSdGW9xn/lNteWW1n2192Dx5Sze2U44I8nLqst11IjTQDQKpZhvtFS/+XV\n1b3T0Zt37FV8C39SEO/c28UyzLdPaOQYjRdhD3dtYxXDkOPLtuQeAjs1Ygi5uanOQMjdbd3H\nVG2cTKX/NDTW6LKftFhvJC1xGblPVRZ3T0XfDU4u84+IkvSEb9hp5D5TXZrVtQEAZNCVdRV3\nrG8MJlI379i7R9E2KD/v7BuKxr+8ulqTG7iXUOOwbSot3Dke3heeUnotR4fATqVqnbZPV5d2\nRmb+PHwMzXif6RtJCuLVDZV6uJO6rLacMzDLn+X3ysj4UDS+ubYM6ToAoMvHy4vuPKVJlKRv\n7WrfolAblPcnIs/1+1s8rs/p8t74moZKlmF+uKfrO7v3393W8/6EehsNIrBTr79rrPKaTf97\noC+SSi/n52O88PzAaKXdelaRN9trU4NCi+mC8qK9oanl3EKJkvR4z5CdYz9TpcevJACg3Ule\n932nr883m36oRBuUhCD8V1u3mWXvWN9o0FMR9pBAIkkIGYsntgVCLw6O3raz7cEDfUovanEI\n7NTLxrE3rqmZSvMPH1zW7/DzA/6ZNH9lfYV+fum+UFfBMsxjPUNH/cmt/uBgNH5pbbmDniGM\nAACHq3XKbVDsj3QP/mdu26A8sL93JJb46pqacpslZy+qHhIh/9XWM+8Nf7p3uFOVxykQ2Kna\n+WWFJ3rzXhwaPephnJQoPtPnL7KYP1pWmJu1qUGpzXJuacHO8fDBJd8fSSKP9QzaOVafFQQA\n0Ayv2XTPaes2FrhfGhr7t/cOJDI0W3xpO8fDLw2ObSxwX1Sl034Cw9G4P5ZY+PjuZW/yziUE\ndmp3c1M9yzD3tveIS96c/WUoEEqmNteW6eqkEiHkC3UVDCGP+5ZK2r02GuyfiX+2usyJdB0A\nUM7GsT86uekT5UXbA6FvvNMWTi5rr85xm07zd+3rtnHst9Y16Ovqcpi0uPj1NyWuaGp5liCw\nU7tqh/XSmrLOyMyLg0c8RSFK0tO9w/JB0VyuTQ1qnbYzivPfHJ3oPcKAXYmQx3oGrSz7+Rqk\n6wBACziGuX1945dXVx+MzNy0PbttUO7v8E0kUzc11RVZNDV5/JiU2y2LnrpTZ4tmBHYUuKah\nqsRq/sXB/skjnKLY6g8OxxKX1pbpc5rCVfWVEiFP9Q4v+m/fGJ3wTcc+V1OaZzLmeGEAANlz\nZV3F7esbxxNJuQ1KXBC2BUJ/GBh9fyKSqd13b4+F/joy3lqUf0F5UWaekU4mg+Gahqp5D7Z4\nXKcXehRZz9JQmaKAmTXcuKb2e+8f+PmBvtvXN877txIhT/cOW1j2Er2e91yT59jodf91ZPyL\nDZXzNvZKhPyme9DCsp+vKVNqeQAAWXJBeVGhxfTd9w58a2eHhTXM8LODKFflOb67YXXpyg46\nRFLpu9u7XUbutpb6TCyWbptry4qs5id8Q33TMa/FdF5pwdX1Feo8IIzAjg4fKfGeVuj5y3BA\nHkpx+L/aEQh1T0Uvry136XgD2VX1FbsnJn/bO/z15g99Ab09NtEzHb2irtyNdB0AaNFJXvcP\nNzZ94522Q1EdIeRgZObfP+j8nzNOONZnG4klHurs3xOKCJJkMjDhZPrfNqzON5syumRanVPi\nPaeEgm5iKMVS46amOqPBcH9H77wT14/7howGw6W1us5IbfDmNXucfx4KTCT/Nk5RIuTX3UNm\n1nBZrcYHrAGAng3HEyKZX3w9EJnZNTEZSqaSwnI3+I/EEjdu2/P6aHAylZ5O8xPJtIFhyu3W\nTK8Xsku/OR7qlNssV9SVP9o9+Pt+/6VzhcW9oan28PRFlSVe3d9RXVVf+c/vdvy2d+TGNTXy\nIzsCoa6pmc21ZUjXAYCGhQ+7oT3cHTvb5f9jNBjsHOswcvL/OjjOYWQdHGc3cg6Olf/XYeSe\n6fPPpPnDn0GUpF93D37/pDVZ/ztA5iCwo8mVdRWvjIz/qmvgvNICOZJ73DdkYJjL65CRIqcV\nehpc9j8MjH6hvkKuSj/aPWhmDZcjXQcAmlZiXWQvHUPIZ6vLRCJFeWEmzUd5fiYtTKbSQ9F4\nlD+G7nddU2rswQtLQGBHEzNr+FpT3T+/2/H99zub3M5Iit81Hj6vtECfrcDnYQj5Ql3F9z/o\n/G3v8Pllhf0z8c7IzOdryrA7BAC0rbXIU2QxyzOvDnsw/6am2iP9kek0P8Pz0bTwt//lhad7\nhwPx5LyfNBqwZYsyCOwos97jyjNx+8J/G5AaSqV5UeIMajybk2OnFHgcHPdYz5A8ZIxhmI+V\n6fqIPgDogYVlf3xK8137ujompwkhDCGbygpvbqpb4o84jZzTyJEPb5+bTvMLp9CeXODO9Hoh\nuxDYUeaxnqFI6kN7ID6YiDw/4Ec7D0LIj/d1HX4uTJKkn+w7+L9nbmBVeSIdACBTqh3W+1vX\nj8QSwUSywm49vl3Xm2vKdgRCh88/rbJbv9RQmbllQi4gsKPMtkBo4YNvB0II7EbjybfGJuY9\n6JuO7Q5OnqrKHpIAABnEEFJus6xkZ46NY3/auv6lobEPJiKiJLV4XBdXlZhQiqUNAjvKxHh+\nsQdzMQda5YZjiw/VGV5scjMAACzEMszFlSUXV5YovRA4fojEKVPtsC3zQb05Un9mPfdtBgAA\nvUFgR5kr6+bPMDEZDJfpuzuxrMHpqFjQSNPGsaegDgsAALqBwI4yJ3rz7jy5qdZpI4QwhKzJ\nc/zXqS31TrvS61Iew5B/3bCqxGo+9IjTyP3LCauQsQMAAP3ANY8+Jxe4f3HWiVFeMDDEyrJK\nL0dFGl2OX5590tuB0GA0Xmwxn1Gcj6gOAAB0BZc9Wtk5hHSLMLOGTaUFSq8CAABAGSjFAgAA\nAGgEAjsAAAAAjUBgBwAAAKARCOwAAAAANAKBHQAAAIBGILADAAAA0AgEdgAAAAAagcAOAAAA\nQCMQ2AEAAABoBAI7AAAAAI1AYAcAAACgEQjsAAAAADQCgR0AAACARiCwAwAAANAIBHYAAAAA\nGoHADgAAAEAjENgBAAAAaAQCOwAAAACNQGAHAAAAoBEI7AAAAAA0gjvqTwiC8NJLLyUSiSV+\nJhaLEUJEUczYugAAAADgGB09sHv11VcvueSS5TxXV1fXitcDAAAAAMeJkSRp6Z9YTsbu1ltv\nTaVSg4ODJpMpo8sDAAAA0LiWlhZCSFtb28qf6uiB3XK0tLT4fL6mpib5HyVJGh0dtVgsK39m\nOJJ4PG61WpVehcbhTc42vMPZhnc4B/AmZ1sikSgpKWEYRumFZFFHR0ddXV1GArujl2KX45JL\nLnn55ZcP/ePo6OjIyEhGnhkAAACgtLRU6SVkUVNT08c//vGMPFVmMnbzPPXUU1dcccU3vvGN\n1tbWjD85EEK2b99+zz334B3OKrzJ2YZ3ONvwDucA3uRsk9/hJ5988vLLL1d6LXTITMZuHoPB\nQAhpbW3dvHlzNp4fCCH33HMP3uFsw5ucbXiHsw3vcA7gTc62e+65R44rYDnwTgEAAABoBAI7\nAAAAAI1AYAcAAACgEQjsAAAAADQCgR0AAACARiCwAwAAANAIBHYAAAAAGoHADgAAAEAjENgB\nAAAAaERWAjt5HDKGImcP3uEcwJucbXiHsw3vcA7gTc42vMPHKiuzYgVB2LJly/nnn8+ybMaf\nHAje4ZzAm5xteIezDe9wDuBNzja8w8cqK4EdAAAAAOQe9tgBAAAAaAQCOwAAAACNQGAHAAAA\noBEI7AAAAAA0AoEdAAAAgEYgsAMAAADQCAR2AAAAABqBwA4AAABAIxDYAQAAAGjE/wcxci95\ntMKzHAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 2604 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  PRKCB, OSBPL3, PLAC8, TMEM156, BMP6, ZNF804A, JCHAIN, SLC12A6, KCNQ5, PLEK \n",
      "\t   RBM47, SLC39A8, AREG, RGS1, MIR155HG, RASGRP3, CD69, HIST1H1D, SIPA1L1, IGHG1 \n",
      "\t   RHEX, GNG2, PIK3R5, IGLV3-1, IGHG3, LY9, IRF4, ATP8B4, MCTP1, DAPK1 \n",
      "Negative:  KAZN, GHR, RBMS3, CHL1, CDH11, DLC1, BMP5, COL6A2, LHFPL6, NRXN3 \n",
      "\t   TMEM108, IGFBP5, CXCL12, APOE, LEPR, NCAM2, NAV2, ANGPTL4, EYA1, DCN \n",
      "\t   EBF1, VCAM1, EBF3, NNMT, FBN1, CFD, COL1A2, EPAS1, BICC1, ABCA8 \n",
      "PC_ 2 \n",
      "Positive:  IGHG1, JCHAIN, IGHG3, IGLV3-1, IGHA1, SCNN1B, IGHG4, IGLC2, TBC1D9, FAAH2 \n",
      "\t   IGHGP, SLAMF1, RASGRP3, LY9, TMEM156, BFSP2, CARMIL1, JSRP1, IGLC3, STAP1 \n",
      "\t   IGHG2, DCC, AC092546.1, NRG3, IGF1, IGHA2, TSHZ2, ITGA8, DNAAF1, IGLV10-54 \n",
      "Negative:  STMN1, TUBB, TYMS, PCLAF, PRSS57, DUT, TUBA1B, MAML3, CENPF, RNF220 \n",
      "\t   ZNF521, HLA-DRA, HIST1H4C, TRIM24, SMC2, SMC4, AC016205.1, PLCB1, SLC39A8, PCNA \n",
      "\t   HELLS, TOP2A, HLA-DRB1, NUSAP1, KIAA1211, GATA2, DIAPH3, HIST1H1D, HIST1H1B, MED12L \n",
      "PC_ 3 \n",
      "Positive:  RGS5, CASQ2, NDUFA4L2, MYH11, PRKG1, MCAM, TINAGL1, RERGL, PHLDA2, RCAN2 \n",
      "\t   LDB3, DGKB, SOD3, CACNA1C, MRVI1, COX4I2, ACTA2, ERBB4, GJA4, ANO1 \n",
      "\t   CCDC3, LMOD1, NTN4, MT1A, ADAMTS4, KCNAB1, PDGFA, ADIRF, SNCG, PDE3A \n",
      "Negative:  CHL1, VCAN, LEPR, NCAM2, CP, EYA1, TMEM132C, TMEM108, VCAM1, NAV2 \n",
      "\t   CXCL12, CCBE1, ABCA8, ANGPT1, SLC1A3, PAPPA, PID1, TNFAIP6, DCLK1, ESM1 \n",
      "\t   CFD, BMP5, TRABD2B, P3H2, BMPER, FMO2, ADAMTSL3, FSTL1, LPL, NLGN1 \n",
      "PC_ 4 \n",
      "Positive:  NRXN3, NTRK2, CRISPLD2, DLC1, C11orf96, RBPMS, COL4A2, SLC7A2, NDUFA4L2, MCAM \n",
      "\t   RGS5, SDK1, ANGPT1, CASQ2, FRZB, PPARG, COL4A1, NR2F2-AS1, KCNAB1, CLSTN2 \n",
      "\t   MYH11, RERGL, PLPP3, TINAGL1, PTN, VCAN, COX4I2, RGS6, PHLDA2, EDNRA \n",
      "Negative:  WIF1, IBSP, MMP16, BGLAP, WDR72, OMD, SPP1, PTPRD, NCAM1, CHAD \n",
      "\t   INSC, SFRP4, IFITM5, LINC01060, DPT, SLC44A5, CADM1, VIT, PDGFD, LRRC15 \n",
      "\t   LRP4, ANO5, ENPP1, SLIT2, PCDH9, RANBP3L, COL13A1, TEX41, RUNX2, PTPRZ1 \n",
      "PC_ 5 \n",
      "Positive:  S100A9, S100A8, DOCK4, CXCL8, ACSL1, PIK3R5, BCL2A1, SIPA1L1, TNFRSF1B, NLRP3 \n",
      "\t   LYZ, KYNU, LCP2, MNDA, C5AR1, SLC11A1, CD93, ITGAX, TYROBP, FCN1 \n",
      "\t   AQP9, G0S2, FCER1G, IL1B, SOD2, AC020656.1, S100A12, HCK, MCTP1, ANPEP \n",
      "Negative:  PRDX2, ANK1, KLF1, GATA1, TMEM156, KCNH2, OSBPL3, ATP5IF1, FAM178B, CARMIL1 \n",
      "\t   AHSP, PIP5K1B, CA1, HBD, TFR2, HIST1H4C, PCLAF, SYNGR1, XACT, RHAG \n",
      "\t   FAAH2, IGLV3-1, GLRX5, REXO2, DUT, UROD, SCNN1B, HIST1H2AG, KIAA1211, NEDD4L \n",
      "\n",
      "19:54:50 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:54:50 Read 9602 rows and found 30 numeric columns\n",
      "\n",
      "19:54:50 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:54:50 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
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      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:54:51 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a6ad6ca30\n",
      "\n",
      "19:54:51 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:54:54 Annoy recall = 100%\n",
      "\n",
      "19:54:54 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:54:56 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:54:56 Commencing optimization for 500 epochs, with 406074 positive edges\n",
      "\n",
      "19:54:56 Using rng type: pcg\n",
      "\n",
      "19:55:07 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:55:07 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:55:07 Read 9602 rows and found 50 numeric columns\n",
      "\n",
      "19:55:07 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:55:07 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:55:08 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a571424fa\n",
      "\n",
      "19:55:08 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:55:11 Annoy recall = 100%\n",
      "\n",
      "19:55:11 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:55:13 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:55:13 Commencing optimization for 500 epochs, with 398546 positive edges\n",
      "\n",
      "19:55:13 Using rng type: pcg\n",
      "\n",
      "19:55:24 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 9602\n",
      "Number of edges: 350299\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.8992\n",
      "Number of communities: 28\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.001...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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rEAAMAY5gQ7b8hWrIi4SgWsOwEAAKYw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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 3201 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  COL1A2, COL6A2, COL3A1, COL5A2, MGP, GLIS3, COL6A1, SPARC, TMEM108, PCOLCE \n",
      "\t   COL1A1, LUM, PTPRG, LIFR, NCAM2, COL6A3, OSMR, CNTNAP2, GLI3, ADAMTS17 \n",
      "\t   FN1, ERRFI1, PPFIA2, ABI3BP, BMPER, PRSS23, WWTR1, IL1R1, SLIT2, IGFBP5 \n",
      "Negative:  RHOH, MCTP2, HLA-DRA, MZB1, HIST1H1D, HLA-DPA1, CD69, HLA-DRB1, HLA-DQB1, KLHL6 \n",
      "\t   STMN1, AFF3, HLA-DPB1, FAM30A, IGLL1, SEC11C, ALCAM, FLT3, KCNQ5, ZNF804A \n",
      "\t   AREG, BLNK, DERL3, BIRC3, TBXAS1, MED12L, TMEM156, GYPC, VPREB3, IRF4 \n",
      "PC_ 2 \n",
      "Positive:  TMEM108, CNTNAP2, NCAM2, FGF7, SFRP1, PAPPA, BMPER, SLC1A3, CP, ADCY2 \n",
      "\t   CNTN1, LBP, ADAMTS17, PDZRN4, TENM4, PTGDS, CFAP69, PTGFR, PLXNA4, SPON1 \n",
      "\t   COL1A2, FNDC1, TNFAIP6, ALDH1A3, PID1, PPFIA2, HP, COL8A1, TF, NRXN3 \n",
      "Negative:  ADGRL4, PTPRB, CXorf36, PCAT19, RAMP3, VWF, LDB2, NOSTRIN, TEK, FLT1 \n",
      "\t   DNASE1L3, CALCRL, TM4SF18, TM4SF1, ACKR1, MYRIP, PIEZO2, ANO2, NR5A2, CLEC1A \n",
      "\t   PODXL, A2M, ADAMTSL1, RNASE1, LINC02147, EMCN, GIMAP7, ABCB1, CD93, RAMP2 \n",
      "PC_ 3 \n",
      "Positive:  CRTAC1, TPPP3, DKK3, TGFBI, CRLF1, NBL1, S100A4, ASPN, NDUFA4L2, DPT \n",
      "\t   ENPP1, OGN, CAPS, COMP, ECM1, IGFBP6, PHLDA2, PRELP, CRIP1, PPP1R14C \n",
      "\t   TNFRSF12A, LINC01411, LRRC15, PDGFA, CDH13, FAM180A, CD70, TSPAN2, MFGE8, FAP \n",
      "Negative:  EMCN, ADGRL4, THSD7A, NCAM2, PTPRB, SNTG2, LDB2, CACHD1, P3H2, PCDH17 \n",
      "\t   CNTNAP2, TMEM108, PCAT19, CXorf36, RAPGEF4, TEK, CALCRL, PDE10A, NOSTRIN, PLPP3 \n",
      "\t   PLCXD3, ADAMTSL1, TGFBR3, ACKR1, VWF, TM4SF18, DNASE1L3, CLEC1A, NR2F1, BMPER \n",
      "PC_ 4 \n",
      "Positive:  DERL3, SEC11C, FKBP11, MZB1, SLAMF7, TNFRSF17, CD27, DCC, IFNG-AS1, PRDM1 \n",
      "\t   SCNN1B, PTP4A3, JSRP1, IGHG3, PAIP2B, IGHG1, AC012368.1, IGLV3-1, TTLL7, ZNF215 \n",
      "\t   DUSP5, AC092546.1, ZBP1, PELI1, STAP1, TMEM156, IGLC2, RASGRP3, FAAH2, TBC1D9 \n",
      "Negative:  STMN1, HMGB2, TYMS, PRSS57, AC084033.3, TRIM24, CENPF, DIAPH3, HELLS, RNF220 \n",
      "\t   PCLAF, NUSAP1, ATAD2, TUBA1B, MKI67, HLA-DRA, TOP2A, RNF130, CENPU, MED12L \n",
      "\t   BRIP1, TPX2, KCNQ5, FLT3, SMIM24, KNL1, ASPM, DTL, KIF11, NEGR1 \n",
      "PC_ 5 \n",
      "Positive:  MYH11, RGS5, MRVI1, DGKB, MCAM, RCAN2, CASQ2, RERGL, TINAGL1, KCNAB1 \n",
      "\t   CCDC3, PRKG1, GJA4, ACTA2, NMNAT2, PDE3A, ENTPD3, MT1A, EBF2, CTNNA3 \n",
      "\t   LAMA3, SOD3, CNN1, ERBB4, MYL9, ANO1, HS3ST2, LBH, NTRK3, MYOCD \n",
      "Negative:  CRTAC1, S100A10, CRLF1, ASPN, DPT, PRELP, COMP, FAP, ECM1, ENPP1 \n",
      "\t   CAPS, COL15A1, CDH13, KDELR3, OGN, LINC01411, IGFBP6, AQP1, FAM180A, FMOD \n",
      "\t   CD70, WNT5B, LRRC15, TSPAN2, TNXB, PPP1R14C, HTRA1, PDLIM4, CLU, NBL1 \n",
      "\n",
      "19:59:24 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:59:24 Read 8651 rows and found 30 numeric columns\n",
      "\n",
      "19:59:24 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:59:24 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
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      "*\n",
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      "*\n",
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      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:59:25 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a7f4aabf3\n",
      "\n",
      "19:59:25 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:59:27 Annoy recall = 100%\n",
      "\n",
      "19:59:28 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:59:30 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:59:30 Commencing optimization for 500 epochs, with 362862 positive edges\n",
      "\n",
      "19:59:30 Using rng type: pcg\n",
      "\n",
      "19:59:39 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "19:59:39 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "19:59:39 Read 8651 rows and found 50 numeric columns\n",
      "\n",
      "19:59:39 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "19:59:39 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
      "\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
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      "*\n",
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      "*\n",
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      "*\n",
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      "*\n",
      "*\n",
      "*\n",
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      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "|\n",
      "\n",
      "19:59:40 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a2316d4e4\n",
      "\n",
      "19:59:40 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "19:59:42 Annoy recall = 100%\n",
      "\n",
      "19:59:43 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "19:59:44 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "19:59:45 Commencing optimization for 500 epochs, with 362432 positive edges\n",
      "\n",
      "19:59:45 Using rng type: pcg\n",
      "\n",
      "19:59:54 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 8651\n",
      "Number of edges: 306120\n",
      "\n",
      "Running Louvain algorithm...\n",
      "Maximum modularity in 10 random starts: 0.9162\n",
      "Number of communities: 26\n",
      "Elapsed time: 0 seconds\n",
      "[1] \"Creating artificial doublets for pN = 5%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 10%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 15%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 20%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 25%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n",
      "[1] \"Creating artificial doublets for pN = 30%\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Defining neighborhoods...\"\n",
      "[1] \"Computing pANN across all pK...\"\n",
      "[1] \"pK = 0.005...\"\n",
      "[1] \"pK = 0.01...\"\n",
      "[1] \"pK = 0.02...\"\n",
      "[1] \"pK = 0.03...\"\n",
      "[1] \"pK = 0.04...\"\n",
      "[1] \"pK = 0.05...\"\n",
      "[1] \"pK = 0.06...\"\n",
      "[1] \"pK = 0.07...\"\n",
      "[1] \"pK = 0.08...\"\n",
      "[1] \"pK = 0.09...\"\n",
      "[1] \"pK = 0.1...\"\n",
      "[1] \"pK = 0.11...\"\n",
      "[1] \"pK = 0.12...\"\n",
      "[1] \"pK = 0.13...\"\n",
      "[1] \"pK = 0.14...\"\n",
      "[1] \"pK = 0.15...\"\n",
      "[1] \"pK = 0.16...\"\n",
      "[1] \"pK = 0.17...\"\n",
      "[1] \"pK = 0.18...\"\n",
      "[1] \"pK = 0.19...\"\n",
      "[1] \"pK = 0.2...\"\n",
      "[1] \"pK = 0.21...\"\n",
      "[1] \"pK = 0.22...\"\n",
      "[1] \"pK = 0.23...\"\n",
      "[1] \"pK = 0.24...\"\n",
      "[1] \"pK = 0.25...\"\n",
      "[1] \"pK = 0.26...\"\n",
      "[1] \"pK = 0.27...\"\n",
      "[1] \"pK = 0.28...\"\n",
      "[1] \"pK = 0.29...\"\n",
      "[1] \"pK = 0.3...\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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h31NQ52SzE8kaURYTAWgCkJdtRXe7AsFbvxIr3eyFUsAFMR7Kiv3avYlWzRB4lm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\nseV3ZWsnIh6f5VVsmiQ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qqI3bRoCIuf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\nPgRJDgUj3f5Qlz/U5QuOv5MKmYwGi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W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\n7AAAAAAAKOL/A0A/CePf+z8BAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "[1] \"Creating 2884 artificial doublets...\"\n",
      "[1] \"Creating Seurat object...\"\n",
      "[1] \"Normalizing Seurat object...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Finding variable genes...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finding variable features for layer counts\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Scaling data...\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Centering and scaling data matrix\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Running PCA...\"\n",
      "[1] \"Calculating PC distance matrix...\"\n",
      "[1] \"Computing pANN...\"\n",
      "[1] \"Classifying doublets..\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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UNg57ZOkpidJ+YEHgBwXW8hB8WKSDsM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cipsmnC3Z/rJYsnrscCp2yw73pgHgp7jQbpEvpjIAACAASURBVLMwY+cnVmA3av8r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\nvCLe3NmG2eLAGkoIJVWbESW3H2S5b49PUxS8ciep3atbgYFd0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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FindDoublets <- function(seurat.rna, PCs = 1:50, exp_rate = 0.02, sct = FALSE){\n",
    "  # sct--do SCTransform or not\n",
    "  \n",
    "  ## pK identification\n",
    "  sweep.res.list <- paramSweep_v3(seurat.rna, PCs = PCs, sct = sct)\n",
    "  sweep.stats <- summarizeSweep(sweep.res.list, GT = FALSE)\n",
    "  bcmvn <- find.pK(sweep.stats)\n",
    "  \n",
    "  ## Homotypic Doublet proportion Estimate\n",
    "  annotations <- seurat.rna@meta.data$seurat_clusters\n",
    "  homotypic.prop <- modelHomotypic(annotations)           ## ex: annotations <- seu_kidney@meta.data$ClusteringResults\n",
    "  nExp_poi <- round(exp_rate * length(seurat.rna$seurat_clusters))  ## Assuming 7.5% doublet formation rate - tailor for your dataset\n",
    "  nExp_poi.adj <- round(nExp_poi*(1-homotypic.prop))\n",
    "  \n",
    "  ## Run DoubletFinder with varying classification stringencies ----------------------------------------------------------------\n",
    "  seurat.rna <- doubletFinder_v3(seurat.rna, PCs = PCs, pN = 0.25,\n",
    "                                 pK = 0.09, nExp = nExp_poi, reuse.pANN = FALSE, \n",
    "                                 sct = sct)\n",
    "  \n",
    "  seurat.rna <- doubletFinder_v3(seurat.rna, PCs = PCs, pN = 0.25, \n",
    "                                 pK = 0.09, nExp = nExp_poi.adj,\n",
    "                                 reuse.pANN = paste0(\"pANN_0.25_0.09_\", nExp_poi), \n",
    "                                 sct = sct)\n",
    "  doublet_var = paste0('DF.classifications_0.25_0.09_', nExp_poi.adj)\n",
    "  seurat.rna[['Doublet_Singlet']] = seurat.rna[[doublet_var]]\n",
    "  \n",
    "  mnames = names(seurat.rna@meta.data)\n",
    "  seurat.rna@meta.data[, grep(mnames, pattern = '0.25_0.09')] <- NULL\n",
    "  #seurat.rna = subset(seurat.rna, Doublet_Singlet == 'Singlet')\n",
    "  return(seurat.rna)\n",
    "}\n",
    "\n",
    "ob.list <- list(MACS, H21, H23,H24,H32,H33,H34,H35, H36, H38, H39, H41)\n",
    "for (i in 1:length(ob.list)) {\n",
    "  ob.list[[i]][[\"percent.mt\"]] <- PercentageFeatureSet(ob.list[[i]], pattern = \"^MT-\")\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = nFeature_RNA > 100 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "  # ob.list[[i]] <- subset(ob.list[[i]], cells = cells.use)\n",
    "  ob.list[[i]] <- NormalizeData(ob.list[[i]])\n",
    "  ob.list[[i]] <- FindVariableFeatures(ob.list[[i]])\n",
    "  ob.list[[i]] <- ScaleData(ob.list[[i]])\n",
    "  ob.list[[i]] <- RunPCA(ob.list[[i]], features = VariableFeatures(object = ob.list[[i]]))\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "  ob.list[[i]] <- FindNeighbors(ob.list[[i]], dims = 1:30)\n",
    "  ob.list[[i]] <- FindClusters(ob.list[[i]], resolution = 1)\n",
    "  ob.list[[i]] <- FindDoublets(ob.list[[i]], PCs = 1:30, sct = FALSE, exp_rate = (length(colnames(ob.list[[i]]))/125000))\n",
    "}\n",
    "\n",
    "MACS <- ob.list[[1]]\n",
    "H21 <- ob.list[[2]]\n",
    "H23 <- ob.list[[3]]\n",
    "H24 <- ob.list[[4]]\n",
    "H32 <- ob.list[[5]]\n",
    "H33 <- ob.list[[6]]\n",
    "H34 <- ob.list[[7]]\n",
    "H35 <- ob.list[[8]]\n",
    "H36 <- ob.list[[9]]\n",
    "H38 <- ob.list[[10]]\n",
    "H39 <- ob.list[[11]]\n",
    "H41 <- ob.list[[12]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d4706e9e-dd46-4375-b665-5ac6c5ccc6f5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(DoubletFinder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057e2fba-e453-4b70-8d9c-9b7be053195b",
   "metadata": {},
   "source": [
    "# Merge all seurat objects together "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "629d916e-d75c-47ea-b824-61a38409b7c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Variable genes: 2000\"\n"
     ]
    }
   ],
   "source": [
    "variable_genes <- VariableFeatures(combined)\n",
    "print(paste(\"Variable genes:\", length(variable_genes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "79cfa2a3-51df-45e8-854a-1499a9a4ffec",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Normalizing layer: counts.H14_MACS\n",
      "\n",
      "Normalizing layer: counts.H21\n",
      "\n",
      "Normalizing layer: counts.H23\n",
      "\n",
      "Normalizing layer: counts.H24\n",
      "\n",
      "Normalizing layer: counts.H32\n",
      "\n",
      "Normalizing layer: counts.H33\n",
      "\n",
      "Normalizing layer: counts.H34\n",
      "\n",
      "Normalizing layer: counts.H35\n",
      "\n",
      "Normalizing layer: counts.H36\n",
      "\n",
      "Normalizing layer: counts.H38\n",
      "\n",
      "Normalizing layer: counts.H39\n",
      "\n",
      "Normalizing layer: counts.H41\n",
      "\n",
      "Finding variable features for layer counts.H14_MACS\n",
      "\n",
      "Finding variable features for layer counts.H21\n",
      "\n",
      "Finding variable features for layer counts.H23\n",
      "\n",
      "Finding variable features for layer counts.H24\n",
      "\n",
      "Finding variable features for layer counts.H32\n",
      "\n",
      "Finding variable features for layer counts.H33\n",
      "\n",
      "Finding variable features for layer counts.H34\n",
      "\n",
      "Finding variable features for layer counts.H35\n",
      "\n",
      "Finding variable features for layer counts.H36\n",
      "\n",
      "Finding variable features for layer counts.H38\n",
      "\n",
      "Finding variable features for layer counts.H39\n",
      "\n",
      "Finding variable features for layer counts.H41\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "PC_ 1 \n",
      "Positive:  RHOH, IQGAP2, MZB1, RAB11FIP1, MCTP2, SEC11C, PLAC8, BIRC3, DERL3, AREG \n",
      "\t   HMGA1, KLHL6, BCL11A, SPN, PLEK, AIF1, TBXAS1, PIK3R5, SLAMF7, JARID2 \n",
      "\t   DUSP2, FKBP11, TMEM156, CD69, FAM30A, HIST1H1D, S100A9, CD27, SLC12A6, S100A8 \n",
      "Negative:  CALD1, KAZN, CDH11, LHFPL6, RBMS3, COL1A2, FBN1, COL6A2, BICC1, DCN \n",
      "\t   NNMT, FSTL1, FBXL7, MAGI2, CHL1, NAV2, COL6A1, TMEM108, MAPK10, SELENOP \n",
      "\t   COL3A1, LAMA4, CFH, LUM, EBF1, BMP5, APOE, VCAN, MGP, LEPR \n",
      "PC_ 2 \n",
      "Positive:  DERL3, MZB1, FKBP11, SLAMF7, TNFRSF17, CD27, SEC11C, IGHG1, IFNG-AS1, LINC02384 \n",
      "\t   CHL1, IGHG3, JSRP1, IGHA1, TMEM108, TMEM156, EYA1, APOE, BMP5, JCHAIN \n",
      "\t   RAB30, NCAM2, FAAH2, IGLV3-1, CARMIL1, CYTOR, IGHG4, BFSP2, PAPPA, CDH11 \n",
      "Negative:  ADGRL4, EMCN, LDB2, VWF, CALCRL, FLT1, PTPRB, TM4SF1, ANO2, RAMP3 \n",
      "\t   TEK, NOSTRIN, CYYR1, PLCB4, CLEC14A, ARHGAP29, PALMD, PLVAP, RNASE1, AQP1 \n",
      "\t   PODXL, RAMP2, PLAT, IFI27, DNASE1L3, GIMAP7, TM4SF18, GNG11, A2M, CXorf36 \n",
      "PC_ 3 \n",
      "Positive:  STMN1, HMGA1, TYMS, PCLAF, CDK6, PRSS57, AC084033.3, CENPF, GNA15, MYB \n",
      "\t   DIAPH3, NUSAP1, TUBB, TOP2A, AC016205.1, ATAD2, HELLS, BCL11A, MKI67, SOX4 \n",
      "\t   DUT, SPN, HIST1H1B, XYLT1, CKS2, HMGB2, CENPK, AIF1, DTL, CENPU \n",
      "Negative:  SEC11C, DERL3, A2M, ITGA6, CADPS2, CAV1, ADGRL4, FKBP11, DUSP5, LDB2 \n",
      "\t   SLAMF7, MZB1, TNFRSF17, EMCN, PTPRB, ARHGAP29, FABP4, TM4SF1, RAMP2, TBC1D9 \n",
      "\t   CD27, KCNN3, GIMAP7, RAMP3, VWF, IFI27, TEK, PLVAP, AQP1, TSHZ2 \n",
      "PC_ 4 \n",
      "Positive:  VCAN, NAV3, NCAM2, P3H2, EYA1, CNTNAP2, CHRDL1, NAV2, LEPR, CP \n",
      "\t   CXCL12, ADAM28, SLC1A3, CHL1, C7, PLXNA4, PAPPA, LINC02384, TMEM132C, VCAM1 \n",
      "\t   PLPP3, SNTG2, TMEM108, TRABD2B, ROR2, TNFAIP6, NLGN1, ADCY2, ALPL, COL28A1 \n",
      "Negative:  CPE, WIF1, IBSP, COL13A1, MAP1B, SFRP4, PRKG1, NCAM1, BGLAP, OMD \n",
      "\t   PTPRD, ADIRF, SRGAP3, MMP16, FAT1, CHAD, WDR72, SPP1, CADM1, BGN \n",
      "\t   LMO7, INSC, CRYAB, S100A13, CLEC11A, LRP4, SOD3, TPM2, TNC, SLC44A5 \n",
      "PC_ 5 \n",
      "Positive:  MYH11, KCNAB1, RGS5, RERGL, MCAM, CASQ2, MRVI1, RCAN2, LDB3, PLN \n",
      "\t   PHLDA2, PDE3A, DGKB, LMOD1, ACTA2, CDH6, NMNAT2, CNN1, CCDC3, COX4I2 \n",
      "\t   TINAGL1, NDUFA4L2, NR2F2-AS1, CTNNA3, EDNRA, MYOCD, SOX5, COL4A5, AKAP6, LBH \n",
      "Negative:  WIF1, NCAM1, IBSP, SFRP4, FRMD4B, MMP16, BGLAP, WDR72, SRGAP3, SPP1 \n",
      "\t   OMD, NRP2, CHAD, LRP4, S100A13, INSC, RUNX2, PTPRD, LMO7, S100A9 \n",
      "\t   DOCK4, S100A8, SLC44A5, SLIT2, PDGFD, COLEC12, IFITM5, CLEC11A, CADM1, DAPK2 \n",
      "\n",
      "11:38:36 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "11:38:36 Read 91060 rows and found 30 numeric columns\n",
      "\n",
      "11:38:36 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "11:38:36 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
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      "\n",
      "11:38:46 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a3d100530\n",
      "\n",
      "11:38:46 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "11:39:15 Annoy recall = 100%\n",
      "\n",
      "11:39:16 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "11:39:20 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "11:39:33 Commencing optimization for 200 epochs, with 4020870 positive edges\n",
      "\n",
      "11:39:33 Using rng type: pcg\n",
      "\n",
      "11:40:19 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim30_ to UMAPdim30_”\n",
      "11:40:19 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "11:40:19 Read 91060 rows and found 50 numeric columns\n",
      "\n",
      "11:40:19 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "11:40:19 Building Annoy index with metric = cosine, n_trees = 50\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "\n",
      "[----|----|----|----|----|----|----|----|----|----|\n",
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      "\n",
      "11:40:29 Writing NN index file to temp file /tmp/RtmprH6Lm8/file3b07a5ba5befd\n",
      "\n",
      "11:40:29 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "11:40:57 Annoy recall = 100%\n",
      "\n",
      "11:40:58 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "11:41:02 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "11:41:12 Commencing optimization for 200 epochs, with 4019480 positive edges\n",
      "\n",
      "11:41:12 Using rng type: pcg\n",
      "\n",
      "11:41:58 Optimization finished\n",
      "\n",
      "Warning message:\n",
      "“Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP_dim50_ to UMAPdim50_”\n",
      "Computing nearest neighbor graph\n",
      "\n",
      "Computing SNN\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck\n",
      "\n",
      "Number of nodes: 91060\n",
      "Number of edges: 3326805\n",
      "\n",
      "Running Louvain algorithm with multilevel refinement...\n",
      "Maximum modularity in 10 random starts: 0.9375\n",
      "Number of communities: 43\n",
      "Elapsed time: 25 seconds\n"
     ]
    }
   ],
   "source": [
    "combined <- merge(x = MACS, y = c(H21, H23,H24, H32, H33,H34,H35,H36, H38, H39, H41), add.cell.ids = c(\"H14_MACS\", \"H21\", \"H23\",\"H24\", \"H32\", \"H33\", \"H34\", \"H35\", \"H36\", \"H38\", \"H39\",\"H41\"), project = \"SB62_NBM\")\n",
    "\n",
    "ob.list <- list(combined)\n",
    "\n",
    "for (i in 1:length(ob.list)) {\n",
    "  ob.list[[i]][[\"percent.mt\"]] <- PercentageFeatureSet(ob.list[[i]], pattern = \"^MT-\")\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = nFeature_RNA > 100 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = Doublet_Singlet == \"Singlet\")\n",
    "  ob.list[[i]] <- NormalizeData(ob.list[[i]])\n",
    "  ob.list[[i]] <- FindVariableFeatures(ob.list[[i]])\n",
    "  ob.list[[i]] <- ScaleData(ob.list[[i]])\n",
    "  ob.list[[i]] <- RunPCA(ob.list[[i]], features = VariableFeatures(object = ob.list[[i]]))\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "  ob.list[[i]] <- FindNeighbors(ob.list[[i]], dims = 1:30)\n",
    "  ob.list[[i]] <- FindClusters(ob.list[[i]], algorithm = 2, resolution = 1)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4931786b-de5b-4642-a367-12bfb59ac5fe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in FindClusters.Seurat(combined, algorithm = 2, resolution = 1.5, : Provided graph.name not present in Seurat object\n",
     "output_type": "error",
     "traceback": [
      "Error in FindClusters.Seurat(combined, algorithm = 2, resolution = 1.5, : Provided graph.name not present in Seurat object\nTraceback:\n",
      "1. FindClusters.Seurat(combined, algorithm = 2, resolution = 1.5, \n .     group.singletons = T)",
      "2. stop(\"Provided graph.name not present in Seurat object\")",
      "3. .handleSimpleError(function (cnd) \n . {\n .     watcher$capture_plot_and_output()\n .     cnd <- sanitize_call(cnd)\n .     watcher$push(cnd)\n .     switch(on_error, continue = invokeRestart(\"eval_continue\"), \n .         stop = invokeRestart(\"eval_stop\"), error = NULL)\n . }, \"Provided graph.name not present in Seurat object\", base::quote(FindClusters.Seurat(combined, \n .     algorithm = 2, resolution = 1.5, group.singletons = T)))"
     ]
    }
   ],
   "source": [
    "combined <- FindClusters(combined, algorithm = 2, resolution = 1.5, group.singletons = T)\n",
    "\n",
    "combined <- ob.list[[1]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "813c2341-48e0-43d4-a26e-8504a6cb15a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "saveRDS(combined, file = \"combined_seurat.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f45e8a22-4474-4499-b65b-d1595f3d8414",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined <- readRDS(\"combined_seurat.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afefef7b-a7ed-47fc-883d-b9b86b8be7f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e9bdefe1-6b6e-4b63-98f9-6a8dae28c2ad",
   "metadata": {},
   "source": [
    "# Find and save all markers for combined UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22ccff8d-590e-44fa-96dc-66f72ff622d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_markers <- FindAllMarkers(combined, max.cells.per.ident = 1000) # Downsample 1000 cells per cluster\n",
    "write.csv(combined_markers, \"SB66_combined_markers.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1166c805-99ed-4821-9cf0-49685e36aecb",
   "metadata": {},
   "source": [
    "# remove contaminating cells and doublets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "303e0525-efe2-4507-a1a9-240fd0cd0d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cluster 46 - muscle\n",
    "# Cluster 41 - CXCL14+ Fibroblasts\n",
    "# Cluster 45 - Doublets Plasma + T\n",
    "\n",
    "combined <- readRDS(\"SB66_combined.RDS\")\n",
    "combined <- subset(combined, subset = seurat_clusters != 46 & seurat_clusters != 41 & seurat_clusters != 45)\n",
    "\n",
    "ob.list <- list(combined)\n",
    "\n",
    "for (i in 1:length(ob.list)) {\n",
    "  ob.list[[i]][[\"percent.mt\"]] <- PercentageFeatureSet(ob.list[[i]], pattern = \"^MT-\")\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], nFeature_RNA > 300 & nCount_RNA > 1000 & nFeature_RNA < 10000 & percent.mt < 10)\n",
    "  ob.list[[i]] <- subset(ob.list[[i]], subset = Doublet_Singlet == \"Singlet\")\n",
    "  ob.list[[i]] <- NormalizeData(ob.list[[i]])\n",
    "  ob.list[[i]] <- FindVariableFeatures(ob.list[[i]])\n",
    "  ob.list[[i]] <- ScaleData(ob.list[[i]])\n",
    "  ob.list[[i]] <- RunPCA(ob.list[[i]], features = VariableFeatures(object = ob.list[[i]]))\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:30, reduction.name = \"UMAP_dim30\", reduction.key = \"UMAP_dim30_\")\n",
    "  ob.list[[i]] <- RunUMAP(ob.list[[i]], dims = 1:50, reduction.name = \"UMAP_dim50\", reduction.key = \"UMAP_dim50_\")\n",
    "  ob.list[[i]] <- FindNeighbors(ob.list[[i]], dims = 1:30)\n",
    "  ob.list[[i]] <- FindClusters(ob.list[[i]], algorithm = 2, resolution = 1.5)\n",
    "}\n",
    "\n",
    "\n",
    "combined <- ob.list[[1]] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a9c796-c8fb-4122-b7fc-b9da3d85ae1c",
   "metadata": {},
   "source": [
    "# Find and save all markers for combined UMAP\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b819d14a-aff0-4b7e-8f9b-8196d87f3073",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_markers <- FindAllMarkers(combined, max.cells.per.ident = 1000) # Downsample 1000 cells per cluster\n",
    "write.csv(combined_markers, \"SB66_combined_markers.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9a77f50-723c-4bae-aace-4dc850d3743d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotate the cells in the final dataset ----\n",
    "\n",
    "# Prepare seurat object for viscello\n",
    "library(VisCello)\n",
    "DefaultAssay(combined) <- \"RNA\"\n",
    "fmeta <- data.frame(symbol = rownames(combined)) \n",
    "rownames(fmeta) <- fmeta$symbol\n",
    "eset <- new(\"ExpressionSet\",\n",
    "            assayData = assayDataNew(\"environment\", exprs=combined@assays$RNA@counts, \n",
    "                                     norm_exprs = combined@assays$RNA@data),\n",
    "            phenoData =  new(\"AnnotatedDataFrame\", data = combined@meta.data),\n",
    "            featureData = new(\"AnnotatedDataFrame\", data = fmeta))\n",
    "saveRDS(eset, \"VisCello/eset.rds\") \n",
    "# Creating a cello for all the cells\n",
    "cello <- new(\"Cello\", name = \"Combined All Cells SB66\", idx = 1:ncol(eset)) # Index is basically the column index, here all cells are included \n",
    "# Code for computing dimension reduction, not all of them is necessary, and you can input your own dimension reduction result into the cello@proj list.\n",
    "# It is also recommended that you first filter your matrix to remove low expression genes and cells, and input a matrix with variably expressed genes\n",
    "cello@proj <- list('PCA' = combined@reductions$pca@cell.embeddings,\n",
    "                   'UMAP_dim30' =combined@reductions$UMAP_dim30@cell.embeddings,\n",
    "                   'UMAP_dim50' =combined@reductions$UMAP_dim50@cell.embeddings)\n",
    "clist <- list()\n",
    "clist[[\"SB66 Global dataset\"]] <- cello\n",
    "saveRDS(clist, \"VisCello/clist.rds\") \n",
    "\n",
    "# Rename clusters after manual inspection of viscello obj and markers csv\n",
    "combined <- SetIdent(combined, value = \"seurat_clusters\")\n",
    "new.cluster.ids.combined <- c(\"Adipo-MSC\", \"Plasma Cell\", \"Plasma Cell\", \"Plasma Cell\", \"THY1+ MSC\", \"Osteo-MSC\",\n",
    "                              \"Neutrophil\", \"SEC\", \"Pro-B\", \"CD4+ T-Cell\", \"Osteoblast\", \"VSMC\", \"HSC\",\n",
    "                              \"Late Myeloid\", \"Late Erythroid\", \"GMP\", \"MEP\", \"OsteoFibro-MSC\",\n",
    "                              \"CD8+ T-Cell\", \"MPP\", \"Early Myeloid Progenitor\", \"GMP\", \"Fibro-MSC\", \"RNAlo MSC\", \"Monocyte\", \"Mature B\",\n",
    "                              \"AEC\", \"Cycling HSPC\", \"Pre-Pro B\", \"Late Myeloid\", \"Erythroblast\", \"Plasma Cell\", \"VSMC\",\n",
    "                              \"Pre-B\", \"pDC\", \"Ba/Eo/Ma\", \"AEC\", \"CLP\", \"Plasma Cell\",\n",
    "                              \"Cycling DCs\", \"RBC\", \"Plasma Cell\", \"MPP\", \"Macrophages\", \"RBC\", \"NKT Cell\")\n",
    "\n",
    "names(new.cluster.ids.combined) <- levels(combined)\n",
    "combined <- RenameIdents(combined, new.cluster.ids.combined)\n",
    "DimPlot(combined, label = TRUE, reduction = \"UMAP_dim30\") + coord_fixed() + NoAxes() + NoLegend()\n",
    "combined$cluster_anno_l2 <- combined@active.ident\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "238c7c4b-e677-474e-84f0-b48f840515e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e12349-d540-4851-a11c-3ed916fcb15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mks do not cluster separately, need to subcluster erythroblasts to properly annotate\n",
    "\n",
    "\n",
    "# subset erythroblast cluster and rename Mks \n",
    "EBs <- subset(combined, cluster_anno_l2 == \"Erythroblast\")\n",
    "EBs <- NormalizeData(EBs)\n",
    "EBs <- FindVariableFeatures(EBs)\n",
    "EBs <- ScaleData(EBs)\n",
    "EBs <- RunPCA(EBs, reduction.name = \"EB_pca\", features = VariableFeatures(EBs))\n",
    "EBs <- RunUMAP(EBs, dims = 1:10, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim10\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:20, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim20\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:30, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim30\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:40, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim40\")\n",
    "EBs <- RunUMAP(EBs, dims = 1:50, reduction = \"EB_pca\", reduction.name = \"EB_UMAP_dim50\")\n",
    "EBs <- FindNeighbors(EBs, dims = 1:20)\n",
    "EBs <- FindClusters(EBs, algorithm = 2, resolution = 0.5, group.singletons = FALSE)\n",
    "DimPlot(EBs, label = TRUE, reduction = \"EB_UMAP_dim50\") + coord_fixed() + NoAxes() + NoLegend()\n",
    "FeaturePlot(EBs, features = c(\"ITGA2B\", \"GATA2\"), reduction = \"EB_UMAP_dim50\", coord.fixed = TRUE) & NoAxes() \n",
    "VlnPlot(EBs, features = c(\"ITGA2B\",\"ITGB3\", \"GATA2\" ,\"GYPC\")) # can see that cluster 4 is most megakaryocytic\n",
    "# Rename Mk cluster based on CD41 expression\n",
    "Mk_ids <- colnames(subset(EBs, subset = seurat_clusters == 4)) # GATA2/CD41/CD61 expression\n",
    "combined$cluster_anno_l2 <- as.character(combined$cluster_anno_l2)\n",
    "combined$cluster_anno_l2[Mk_ids] <- \"Megakaryocyte\" \n",
    "combined$cluster_anno_l2 <- as.factor(combined$cluster_anno_l2)\n",
    "levels(combined$cluster_anno_l2)\n",
    "DimPlot(combined, label= TRUE, repel = TRUE, group.by = 'cluster_anno_l2') + NoAxes() + coord_fixed()\n",
    "\n",
    "# remove NKT cluster because they are likely doublets (T-cell markers, Osteolineage markers co-expressed)\n",
    "combined <- subset(combined, cluster_anno_l2 != \"NKT Cell\")\n",
    "\n",
    "# Make coarse annotations\n",
    "cluster_anno_coarse <- c(\"Mesenchymal\", \"Endothelial\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Mesenchymal\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Mesenchymal\", \"Mesenchymal\", \"Mesenchymal\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Hematopoietic\", \"Mesenchymal\", \"Endothelial\", \"Mesenchymal\", \"Muscle\")\n",
    "combined <- SetIdent(combined, value = \"cluster_anno_l2\")\n",
    "names(cluster_anno_coarse) <- levels(combined)\n",
    "combined <- RenameIdents(combined, cluster_anno_coarse)\n",
    "DimPlot(combined, label = TRUE, reduction = \"UMAP_dim30\") + coord_fixed() + NoAxes() + NoLegend()\n",
    "combined$cluster_anno_coarse <- combined@active.ident\n",
    "coarse_cols <- c('#f88379','#FFA750','#6495ED','#B8242D')\n",
    "DimPlot(combined, label = TRUE, reduction = \"UMAP_dim30\", cols=coarse_cols) + coord_fixed() + NoAxes() + NoLegend()\n",
    "\n",
    "# save cluster_anno_l2 colors\n",
    "cal2_cols <- c(\"#FFD580\", \"#FFBF00\", \"#B0DFE6\", \"#7DB954\", \"#64864A\", \"#8FCACA\", \"#4682B4\", \"#CAA7DD\", \"#B6D0E2\", \"#a15891\", \"#FED7C3\", \"#A8A2D2\", \"#CF9FFF\", \"#9C58A1\", \"#2874A6\", \"#96C5D7\", \"#63BA97\", \"#BF40BF\", \"#953553\", \"#6495ED\", \"#E7C7DC\", \"#5599C8\", \"#FA8072\", \"#F3B0C3\", \"#F89880\", \"#40B5AD\", \"#019477\", \"#97C1A9\", \"#C6DBDA\", \"#CCE2CB\", \"#79127F\", \"#FFC5BF\", \"#ff9d5c\", \"#FFC8A2\", \"#DD3F4E\")\n",
    "cal2_col_names <- c(\"Adipo-MSC\", \"AEC\", \"Ba/Eo/Ma\", \"CD4+ T-Cell\", \"CD8+ T-Cell\", \"CLP\", \"Cycling DCs\", \"Cycling HSPC\", \"Early Myeloid Progenitor\", \"Erythroblast\", \"Fibro-MSC\", \"GMP\", \"HSC\", \"Late Erythroid\", \"Late Myeloid\", \"Macrophages\", \"Mature B\", \"Megakaryocyte\", \"MEP\", \"Monocyte\", \"MPP\", \"Neutrophil\", \"Osteo-MSC\", \"Osteoblast\", \"OsteoFibro-MSC\", \"pDC\", \"Plasma Cell\", \"Pre-B\", \"Pre-Pro B\", \"Pro-B\", \"RBC\", \"RNAlo MSC\", \"SEC\", \"THY1+ MSC\", \"VSMC\")\n",
    "names(cal2_cols) <- cal2_col_names\n",
    "\n",
    "combined <- SetIdent(combined, value = \"cluster_anno_l2\")\n",
    "combined_markers_anno <- FindAllMarkers(combined, max.cells.per.ident = 500) # Downsample 1000 cells per cluster\n",
    "write.csv(combined_markers_anno, \"SB66_combined_markers_annotated.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd9c30c-088d-4174-9828-68e911bcc1cf",
   "metadata": {},
   "source": [
    "# Figure 1 Generation ----\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9505c1a5-2e3c-4efd-8649-bf01b097ade1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure 1 Generation ----\n",
    "\n",
    "# in text QC metrics\n",
    "combined@meta.data %>% dplyr::summarise(c(mean(nCount_RNA), mean(nFeature_RNA), mean(percent.mt)))\n",
    "\n",
    "\n",
    "# Supplemental Figure S1C QC Metrics -----\n",
    "VlnPlot(combined, group.by =\"cluster_anno_coarse\", features = c(\"nFeature_RNA\", \"nCount_RNA\", \"percent.mt\"), pt.size=0) & scale_fill_manual(values =c(\"#AEC7E8\", \"#FFBB78\", \"#98DF8A\", \"#FF9896\"))\n",
    "VlnPlot(combined, group.by =\"cluster_anno_coarse\", features = c(\"nCount_RNA\"), pt.size=0, y.max = quantile(combined$nCount_RNA,0.99)) & scale_fill_manual(values =c(\"#AEC7E8\", \"#FFBB78\", \"#98DF8A\", \"#FF9896\"))\n",
    "\n",
    "combined@meta.data %>% dplyr::group_by(cluster_anno_coarse)  %>% summarise(median(nFeature_RNA), median(nCount_RNA), median(percent.mt)) %>% gt() -> supp_fig_1D\n",
    "gtsave(supp_fig_1D, filename = \"~/Documents/Manuscripts/NBM_Atlas/Figures/Supplemental_Figures/Related_to_Figure1/Supp_Fig_1D.pdf\")\n",
    "\n",
    "# Supplemental Figure S1D UMAP Feature Plots -----\n",
    "p1 <- FeaturePlot(object = combined, raster = TRUE, raster.dpi = c(1028,1028), features = c(\"CXCL12\",\"NCAM1\",\"CDH5\", \"PTPRC\", \"MZB1\", \"CSF3R\"), cols = brewer.pal(n = 100, name = \"Reds\"),ncol = 6, max.cutoff = 'q99', coord.fixed = TRUE) & NoAxes() \n",
    "p1_raster <- rasterize(p1)\n",
    "ggsave(p1_raster,file = \"~/Documents/Manuscripts/NBM_Atlas/Figures/Supplemental_Figures/Related_to_Figure1/PanelE_scRNA_MarkerFeaturePlots.pdf\", device = \"pdf\", width = 15, height = 2)\n",
    "\n",
    "\n",
    "# Figure S1E CODEX Cell Type Frequencies Per Sample By Age ----\n",
    "ggplot(data=percentage_data, aes(x=Age,y=percentage, fill=cluster_anno_l1)) +\n",
    "  geom_bar(position=\"fill\",stat=\"identity\") + \n",
    "  theme_bw() + \n",
    "  labs(y='Cell Type Frequency') + \n",
    "  labs(x='') + scale_fill_manual(values=cal1_cols) + theme(axis.text = element_text(size=16)) + ggtitle(\"CODEX Cell Type Frequencies Per Sample\") \n",
    "\n",
    "# perform linear regression testing\n",
    "linear_regression_results <- percentage_data %>%\n",
    "  group_by(cluster_anno_l1) %>%\n",
    "  do(model = lm(percentage ~ Age, data = .))\n",
    "\n",
    "# Extract p-values and R-squared values\n",
    "result_df <- linear_regression_results %>%\n",
    "  summarise(\n",
    "    Cell_Type = cluster_anno_l1,\n",
    "    P_Value = coef(summary(model))[, \"Pr(>|t|)\"][2],\n",
    "    R_Value = summary(model)$r.squared\n",
    "  )\n",
    "\n",
    "# confirm everything adds up to 100 per patient\n",
    "percentage_data %>% group_by(orig.ident) %>% summarise(sum(percentage))\n",
    "\n",
    "# repeat for MSCs\n",
    "\n",
    "# plot cell types as function of age\n",
    "percentage_data <- MSCs@meta.data %>%\n",
    "  group_by(orig.ident, cluster_anno_l2) %>%\n",
    "  summarise(count = n()) %>% \n",
    "  mutate(percentage = count / sum(count) * 100)\n",
    "\n",
    "percentage_data <- left_join(percentage_data, additional_md, by = \"orig.ident\")\n",
    "\n",
    "# Create the ggplot2 plot\n",
    "ggplot(percentage_data, aes(x = Age, y = percentage, color = cluster_anno_l2)) +\n",
    "  geom_line() + geom_point() +\n",
    "  labs(title = \"Cell Type Percentages by Age\",\n",
    "       x = \"Age\",\n",
    "       y = \"Percentage\") +\n",
    "  scale_fill_brewer(palette = \"Set1\") +  # Choose a color palette\n",
    "  theme_minimal()\n",
    "\n",
    "linear_regression_results <- percentage_data %>%\n",
    "  group_by(cluster_anno_l2) %>%\n",
    "  do(model = lm(percentage ~ Age, data = .))\n",
    "\n",
    "# Extract p-values and R-squared values\n",
    "result_df <- linear_regression_results %>%\n",
    "  summarise(\n",
    "    Cell_Type = cluster_anno_l2,\n",
    "    P_Value = coef(summary(model))[, \"Pr(>|t|)\"][2],\n",
    "    R_Value = summary(model)$r.squared\n",
    "  )\n",
    "print(result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d6aeb58-3e8c-41c8-bad5-c87a77120efe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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